スカイロジック List of Products and Services

We received an inquiry from a company that produces labels and POP materials, and we will verify the printing defects. First, we asked them to visit our company for an explanation of our inspection software, leading to a simplified verification process. Our company also accommodates web meetings for those who cannot visit in person. To prevent the work's backing from lifting and affecting the inspection, we pressed down on it with a transparent glass plate while taking photos. If we took photos with the backing still lifted, we detected many discrepancies compared to the master. We covered the "comma" in the print with black to eliminate it and used the "comparison with master" feature of EasyInspector when it reappeared for verification. As a result, we were able to detect the comma. Since the detected value was less than 10 pixels, which is very small, if we want to detect such defects, we need to be careful about the position of the sample when placing it (to minimize misalignment) and avoid detecting discrepancies due to changes in lighting.

• Visual Inspection Equipment

The manufacturer we consulted this time is a company that has been inquiring with us for some time. They sent us sample images. Our company also conducts usage explanations and demonstrations via web conference. First, it would be smoother to enter the verification process after asking about the client's issues and operational status during the web conference. ■ Inspection Settings and Results We conducted learning and processing on the two types of images provided, one with spectral lighting and the other with general lighting. For both images, we specified abnormal areas and trained the system to identify them. In the spectral lighting image, we were able to detect some potentially defective areas, and I feel there is a possibility to improve detection accuracy by increasing the training data. In the general lighting image, it honestly seems difficult to make judgments. It appears challenging to detect anything other than large, grainy defects like those in the image on the right. The left image shows the detection of defects in the spectral lighting image. The numbers in the detection image represent the AI's confidence level in percentage, which we refer to as the number of recognition points.

• Visual Inspection Equipment

By attaching cameras to the devices or equipment you want to monitor, the following can be achieved: ■ The monitored objects can be processed for images at regular intervals, allowing for the determination of normal/abnormal conditions and the recording of read values for each instrument, similar to human patrols. ■ The latest images and historical data of the monitored objects can be checked anytime from terminal devices within the LAN. ■ In the event of abnormal detection, images can be sent via email to multiple smartphones, and outputs can be triggered through buzzers or lamps. ■ A large number of cameras (from a few to about 100; for more, please consult us) can be set up, with multiple (from one to several dozen) reading and confirmation points configured within the images of each camera. ■ It supports socket communication with SCADA and is equipped with an interface that considers compatibility with existing production systems.

• Surveillance Camera System

Checking equipment located at a distance involves the hassle of moving and the risk of accidents during transit. Additionally, it is often difficult to check frequently, and it requires considerable effort for recording and management. ■ Control panel lamps, instruments, and switches ■ Monitoring pressure, temperature, voltage, and valves in the plant ▼ Automatic monitoring, recording, and early abnormality detection using network cameras An image processing system using network cameras can automatically read and record various equipment and meters located at a distance. By allowing for frequent checks, it enables the early detection and response to abnormalities. Furthermore, not only can it read meters, but it can also automatically detect abnormalities such as valve orientation, leaks, and turbidity of treated water through AI image processing. It can also be applied to sensory judgments that require human confirmation, comprehensively realizing the concept of "no need to go check" for patrol inspections.

• Visual Inspection Equipment

Until now, the focus has primarily been on the utilization of equipment within factories and manufacturing sites, but with AI capabilities, it has become possible to detect humans and animals, allowing for applications in various industries such as agriculture. This contributes to reducing time and effort through verification. ■ Management of control panels in greenhouses Automates visual patrol checks using cameras and image processing. ■ Measures against wildlife damage Ignores humans and vehicles, detecting only birds and animals. ▼ Image collection possible in areas without power In forested areas and farmland without power, it is possible to supply power using solar batteries and collect images via mobile phone lines. Real-time notifications can be sent regarding damage from wild boars and deer, as well as illegal dumping. 【Functionality and monitoring examples】 Analog meter reading Reading flow meters, float meters, etc. Digital display (7-segment) numerical reading Lamp status (such as in control panels) Dot matrix numerical reading Reading values and displays from centralized management PC monitors Reading vertical rotation power meters and difficult-to-read characters Monitoring absence of people and entry into restricted areas Detection of insect contamination and animal intrusion Condition of liquids

• Visual Inspection Equipment

Insects, plastic, frogs, anisakis, hair, and other foreign objects are monitored at a speed of about 10 times per second. AI eyes efficiently detect foreign objects on behalf of human eyes and notify with lights or buzzers. When connected to a PLC, it is also possible to stop or discharge the conveyor upon detection of foreign objects. ▼ Easy installation on conveyor belts Even in places where it's difficult to place a PC, the aiforce-mini can be installed and used on the side of the conveyor or control panel. ▼ Detectable foreign objects Insects, plastic pieces, frogs, anisakis, hair, and other items that can be visually confirmed. ▼ Beyond foreign objects Through learning, it can also check for scratches, damage, burns, and chips on the conveyor. 【Product Composition】 - aiforce-mini main unit (AI software pre-installed) - Industrial camera - Lens (specifications are selected based on the inspection target and shooting distance) - USB cable for the camera (3m) * A mouse, keyboard, and monitor needed for setup are optional.

• Visual Inspection Equipment

With the development and spread of AI, it has become possible to automatically detect foreign objects (such as insects, frogs, anisakis, hair, etc.) in food that were previously considered difficult to identify. AI can discover foreign objects that are often overlooked during quick tasks. ■ Hair contamination in food ■ Confirmation of ingredients in cup noodles ■ Identification of bread types on trays ■ Differentiation of meat cuts ■ Counting packaged sweets ■ Inspection of egg cracks ▼ Preventing oversights with AI to ensure food safety Issues such as damage or rot in agricultural products, ingredient shortages in food, and foreign object contamination cannot be completely prevented even with visual checks, leading to oversights. AI continuously monitors agricultural products and food without fatigue, efficiently reducing the chances of missing anything. ▼ Standardization and labor-saving in sorting Agricultural products have various criteria for checks, such as damage and insect bites, and also need to be sorted based on color and shape. When done by human hands, there is inevitably some oversight and subjective judgment, making it difficult to maintain consistent quality. The AI's vision checks and sorts based on constant standards, contributing to the standardization and labor-saving of sorting processes.

• Visual Inspection Equipment

Do you have any of these concerns? ■ It takes a lot of time for manual work ■ Grade classification is difficult, leading to oversights in visual inspections ■ Relying on hired labor increases labor costs ■ While skilled workers are aging, there are no successors Automate with image inspection software! 1) The defect detection tasks that were done manually can be automated ⇒ Detect scratches, discoloration, dirt, etc., and reduce work time! 2) Difficult grade classification can be easily done by anyone ⇒ Various rules can be used for differentiation! Free yourself from manual work! 3) Operate from a single computer! Start at a low cost ⇒ Significantly reduce implementation costs! Keep the number of workers to a minimum and reduce labor costs! Prevent oversights in visual inspections and enhance quality value! You can start small without the need for large machinery! 【Inspection Examples】 ■ Classification of tomatoes by size ■ Classification of flower types ■ Identification of corn seeds ■ Differentiation of tea leaf types ■ Inspection of orange blemishes ■ Detection of defects in coffee beans *For more details, please see the related links.

• Visual Inspection Equipment

This is something that our sales engineers explain almost every time they talk to customers, but despite being a crucial factor that greatly influences AI performance, many of the discussions found online are too general to be practically useful. We thought that if we could provide a more practical, specific, and realistic explanation, it would surely be helpful to everyone, which is the background of this blog. ■ No matter how good the AI is, if it is taught incorrectly, it will produce incorrect results - About teacher images - About annotations - Convenient features in annotations - Blogs and videos that are helpful *For more details, please see the related links (blog).

• Visual Inspection Equipment

This time, I would like to verify the "differences in learning and inspection speed using GPUs" as stated in the title. I will compare the differences in learning and inspection times with two types of GPUs and 16GB and 8GB of RAM. ■Conditions ■Verification Configuration ■Results *For more details, please see the related link (blog).

• Visual Inspection Equipment

We often receive requests to use high-resolution cameras to improve detection capability. In the case of rule-based image processing, using high-resolution images tends to improve resolution and enhance detection capability, but this is not always the case with deep learning. Below is a brief explanation of the perspective on resolution in deep learning, albeit in a rough manner. Let's consider images like (1) to (3) in Figure 1. (1) Total area 10×10, area of the gray rectangle 4 (2) Total area 20×20, area of the gray rectangle 16 (3) Total area 10×10, area of the gray rectangle 16 The area of the gray rectangle in (2) is four times larger than in (1), but when looking at the ratio of the gray rectangle to the total area, (1) is 4/100 and (2) is 16/400, both representing only 4%. In terms of "ease of detecting the gray rectangle" in deep learning, (1) and (2) are almost the same.

• Visual Inspection Equipment

EasyInspector2 has three text recognition features. 1) OCR (Optical Character Recognition) 2) Machine Learning OCR 3) AI OCR ■Simple setup ■Can read characters that were previously difficult to recognize ■Finally *For more details, please see the related link (blog).

• Visual Inspection Equipment

We sometimes receive inquiries from customers regarding the specifications of PCs when connecting multiple devices. This time, we would like to introduce the behavior when connecting multiple cameras using the AI function of EasyInspector2 (hereinafter referred to as EI2). We hope this will serve as a reference when selecting a PC. Additionally, we hope it will also provide guidance on the expected increase in inspection time when connecting multiple devices. We used a multi-controller (hereinafter referred to as EIMC) to start and inspect six EI2 units. ■PC ■Hardware Configuration ■Software Configuration ■Results *For more details, please refer to the related links (blog).

• Visual Inspection Equipment

Recently, we have been receiving more inquiries from customers using AI software (such as EasyInspector2 and DeepSky) asking questions like, "Why does this happen?", "Isn't it supposed to be like this?", and "What is going on inside?". In such cases, it seems that the person in charge is unsure about how to explain things. Indeed, when trying to provide an intuitive and easy-to-understand explanation, they often end up relying on analogies, or if they attempt to explain in detail, they find that specialized books provide more thorough information. I have also been unable to find suitable explanatory materials for customers who want to take a deeper dive into the principles of AI image processing.

• Visual Inspection Equipment

▼Obtaining Anisakis → Until Filming This time, we conducted a verification at our company to find "Anisakis," which we received several inquiries about from customers, using AI. We started by trying to obtain live Anisakis, but it turned out to be more difficult than we expected... We visited several supermarkets and fish shops in the city and made phone calls to request their help. Despite it being an extremely busy time of year, I made a strange request to unknown housewives, saying, "Can you give me live Anisakis for an experiment to find Anisakis with AI (to summarize)?" I am very grateful to the supermarkets that cooperated with us, and to the staff in the fresh fish section who helped us. Thank you very much. We assembled the equipment assuming an operation where we conduct AI Anisakis inspections on a slowly moving conveyor, and if detected, a buzzer would sound to stop the conveyor. Anisakis glows in response to specific wavelengths of ultraviolet light, so we use UV LED light sources and filters that pass specific wavelengths. In image processing, it is also important to capture images that make it easier to find Anisakis through optical manipulation.

• Visual Inspection Equipment

Until now, Skylogic has primarily handled image inspection projects focused on industrial products. This is because the nature of industrial products, which involves "mass-producing the same item," matched well with conventional (rule-based) image processing methods. Conversely, it has been difficult to handle items with unstable colors and shapes, even among industrial products, using traditional methods. However, with the emergence of image processing methods utilizing AI (deep learning), items with variability can now be included in the scope of image processing. This is because AI excels at "detecting only what we want to find while ignoring trivial changes." Now, connecting this to the title, when we talk about "items with unstable colors and shapes," we are referring to food products. Issues such as material defects, foreign object contamination, quantity defects, and missing components... even when the subject is food, products produced in factories are likely to face similar challenges as industrial products. However, the difficulty of processing with traditional methods has led to these issues being abandoned. (And I have also given up on them.) Why not try AI?

• Visual Inspection Equipment

Currently, I am learning a lot about images, and I realize the importance of "taking good pictures." Cameras come equipped with various functions, and it seems that if you can set them correctly, you can capture good images. One of those settings is "white balance." As someone who was a novice in image processing, I had heard of it but wondered what it actually does. After looking into it, I found it interesting and potentially useful, so I would like to share it. *For more details, please see the related links.*

• Visual Inspection Equipment

EasyMonitoring, released in 2018, has been renewed alongside the image processing software "EasyInspector," and was re-released last autumn as EasyMonitoring2. Over the past year, we have introduced features that our customers have appreciated and new capabilities that have been added. With the addition of AI features... It has become possible to detect objects even in environments where monitoring was previously difficult due to significant changes. - Detection with stable accuracy even outdoors - Changes in brightness (trained in advance with images from various times of day and patterns) - The positions where objects appear are varied and different each time Each object can be identified and detected, allowing us to determine "where and what" has been detected. - Detection is acceptable or not based on specific areas Objects are detected with a sense close to that of the human eye. - Relative learning of objects with individual differences (animals, insects, agricultural products, foreign substances, etc.) - Detection of areas that cannot be judged solely by color (RGB) variations or roughness.

• Visual Inspection Equipment

The name "cazoeTell2" has been changed to "wakeTell." The reason for the renaming is that the name "2" often leads customers to mistakenly think it is a superior version of cazoeTell. cazoeTell is specialized in counting a single item and has high counting performance. wakeTell can classify and count multiple items, but its counting performance is lower than that of cazoeTell. ■ Comparison Table (Please refer to the related link (blog)) ■ Comparison of Counting Nuts and Washers Both cazoeTell and wakeTell can count a total of "30" items without any issues. However, cazoeTell only provides the result that there are "30" items without specifying how many of each item there are. On the other hand, wakeTell recognizes and displays the result as "15" nuts and "15" washers. Therefore, wakeTell can also be used for purposes such as forgetting to return tools.

• Visual Inspection Equipment

We have received a lot of inquiries from customers and have made improvements, resulting in an even more upgraded version compared to the initial release! We are now able to achieve higher precision counts for various objects. Before using cazoeTell for counting, it is necessary to create a learning model tailored to the objects you wish to count. 【Creating a Learning Model】 1) Capture teacher images 2) Perform annotation to create training data 3) Train the AI (create training data) ■Points to note when capturing teacher images ■About annotation ■Tips for annotation "If you're curious but unsure if you can do it," or "I'm having trouble with counting!" Customers interested in cazoeTell should first contact Sky Logic! We will create a learning model tailored to the objects you want to count. *For more details, please refer to the related links (blog).

• Visual Inspection Equipment

Recently, we have been receiving inquiries asking if the inspection of agricultural products we are currently conducting can be made a bit easier. Using vegetables provided by local farmers, we tested whether we could: ■ Classify potatoes by grade ■ Detect damage on tomatoes using SkyLogic. ▼ Potato Classification We photographed potatoes classified from A to C from different angles (a total of 66 images were taken), and after creating the training data and conducting inspections, we were able to classify them into grades A, B, and C. (All 30 images were classified correctly, achieving a 100 percent accuracy rate.) ▼ Detecting Damage on Tomatoes Since it was difficult to find damaged tomatoes, we tested whether we could detect damage on one type. Similar to the potatoes, we photographed them from different angles (12 images), created training data, and conducted inspections, resulting in the detection of damage as shown in the images. (All 30 images were inspected, and all damage was detected, achieving a 100 percent accuracy rate.)

• Visual Inspection Equipment

This time, I will introduce information regarding annotations. Annotations are one of the important settings related to detection accuracy, so I hope you find this useful. Q. Should labels be grouped together or divided into finer categories? When there are multiple detection targets (such as scratches, dirt, and dents), there are two patterns: registering all annotations under the same label "defect" or dividing the labels by shape as "scratches, dirt, dents." DeepSky tends to show improved detection results with fewer labels, so it is generally better to register them under the same label. However, if it is necessary to know which defect has been detected, it is essential to separate the labels by shape. In this case, if a scratch is mistakenly registered as dirt, it can lead to inconsistencies during training, resulting in poor learning outcomes. Therefore, annotations must be carried out carefully to avoid mistakes and oversights. (Figure 1)

• Visual Inspection Equipment

During the verification and after the implementation, we received many valuable questions and reports from users regarding the settings of DeepSky, so we would like to introduce them. Q. How do you determine overfitting? Q. What does a convergence of 0.1 or lower mean? Does it become harder to converge as the number of labels increases? Q. What is the difference between continuing training from a certain point and resetting and retraining? *For more details, please refer to the related links (blog).

• Visual Inspection Equipment

DeepSky has the capability to communicate the coordinates of detected objects to a higher-level system via TCP/IP, enabling various applications by tracking the coordinates of objects within that system. Here, we introduce software that inspects objects flowing on a conveyor and counts them when they cross the center of the camera. The diagram below (Figure 2) illustrates the configuration of the conveyor, camera, and software. The camera's images are processed by DeepSky to detect the type and coordinates of the objects, and this information is passed to the upper counting software. The counting software tracks the objects and increments the count when they cross the center of the image. This is an image of products moving on a belt conveyor. The upper camera captures images while checking for defective items and counting the products. It can also interact with PLCs to reduce speed or trigger a buzzer when approaching a specified count. The counting software tracks the coordinates of the objects to count them. In this way, DeepSky can work in conjunction with higher-level software, allowing for various uses. Custom software, like the counting software, can also be developed by our company.

• Visual Inspection Equipment

When talking with customers, I sometimes feel that they believe AI (deep learning) automatically determines what is OK or NG. This is a subtle nuance, and it's not something that needs to be corrected in the course of actual conversation, but today I want to clear up that confusion by writing this article. Now, regarding the confusion mentioned above, the actual difference is that it is humans who decide what is considered OK and what is NG in the software. This leads to the question, "So what does AI do then...?" To put it simply, what our AI does is "find what it has been taught," and fundamentally, that’s all there is to it. This function is generally referred to as "object detection." In "object detection," it literally detects "objects" within an image.

• Visual Inspection Equipment

In recent years, with the miniaturization and precision of machinery and equipment, smaller components are often used, and when performing image inspection of such small-sized parts, high-resolution cameras are increasingly utilized for imaging. However, when using high-resolution cameras, a high-spec computer is also required to connect the cameras. Therefore, in this report, we would like to present the operational verification and results of connecting two high-resolution (14 million pixels) cameras to a single computer and starting them simultaneously. As a result of the verification, we were able to display the screens of both 14 million pixel cameras on a single computer screen. (Figure 1) Additionally, the image below shows the image captured by one of the two connected cameras. It is possible to detect parts that are less than 2mm (approximately 0.8×1.6mm) within a field of view of about 100mm square. (Figure 2 shows an enlarged image of the part outlined in red in the overall image on the left.)

• Visual Inspection Equipment

This time, I would like to organize the types of AI. A new introduction page for DeepSky has also been created, so please take a look. Now, the term "AI" that is currently in the spotlight refers to "deep learning," which is the type of AI that can distinguish between pedestrians and traffic signs, and even defeat human professionals in chess and Go. In the world of image processing, there are also machine learning-based image processing and what is called procedural image processing. So, what are the differences between them? I think it will be easier to understand the meanings of each by looking at the diagram below. AI (Artificial Intelligence), as the name suggests, means artificial intelligence and has been the most widely used concept since around 1950, meaning "something that replaces human intelligence." In the world of image processing, "procedural" (rule-based) image processing, which processes images taken by a camera according to a set procedure to determine OK/NG, also falls under this category of AI. Our EasyInspector uses this type of AI across a wide range, including color judgment and dimensional angle inspection.

• Visual Inspection Equipment

We have received several inquiries from various food production and processing sites. We are now able to handle cases that were difficult for EasyInspector (formerly EasyInspector), such as detecting hair and foreign objects mixed in bento boxes, which further demonstrates the wide range of capabilities of our AI (deep learning) functions. DeepSky allows us to teach it with a broader scope regarding what we want to detect, making it possible to identify foreign objects that do not conform to a specific shape. If there are items that are currently being checked by human eyes and you wonder, "Can this be inspected?" please feel free to contact us.

• Visual Inspection Equipment

▼Feature: Auto Annotation This is a function that automatically executes annotations. It was previously a beta feature, but it has become an official feature starting from Ver. 2.2.0.0. Many of you may know that annotations are very important in object recognition. However, when there are many target objects in an image, the amount of work required for annotation increases, and as the workload increases, mistakes such as forgetting to annotate or making errors inevitably rise. If annotations are forgotten, DeepSky adjusts parameters to determine that "this is something that should not be found (even though it looks similar) for the same object," which can significantly lower the recognition rate of the object, leading to various issues. (See Figure 1: Annotation Forgetting) This is where the auto annotation feature comes into play. By using auto annotation, with just a click of a button, it automatically suggests annotations like "Based on the previous training data, you would want to annotate here, right?" (See Figures 2, 3, and 4)

• Visual Inspection Equipment

Function: OK/NG Judgment by Area This is a function that judges not only "how many items were detected in the image" but also "where they were detected." Depending on the application, it may not be possible to make a correct judgment based solely on the total count on the screen. For example, in the case where there are two capacitors positioned alternately, as shown below. In this case, if the orientations of the capacitors are reversed, they should be deemed unacceptable; however, as for the total count, there would be one with the polarity mark on top and one with it on the bottom, resulting in both being considered acceptable. (Figure 1) In such cases, judgments are made by dividing into areas. As shown in the dotted box below, predetermined areas are set up, and the count judgment is performed for each area. (Figure 2) Within each area: "If one upward-facing capacitor is detected, it is OK." "If even one downward-facing capacitor is detected, it is NG." Settings like these are made. (Figure 3) This allows for correct NG judgment even for defective items that have the same count but are positioned alternately. (Figure 4)

• Visual Inspection Equipment

DeepSky is an image inspection software that uses so-called AI (Deep Learning). By training it on the parts you want to detect, the software adjusts its own setting parameters and learns to recognize them. Here are three features that specifically differentiate it from traditional methods. *We will use the inspection of washer scratches as an example.* ▼ No need for positioning of the inspection target (Figure 1: No scratches, OK) (Figure 2: Scratches, NG) The fact that positioning is unnecessary means that inspections can be performed with the same settings even if the number of washers on the screen varies. This is effective for inspection targets that are difficult to secure. ▼ Can detect even with changes in brightness (Figure 3) It can detect scratches even when the lighting is this dim. This is effective in cases where it is difficult to suppress reflections from metal parts or when dealing with products that come in multiple colors.

• Visual Inspection Equipment

We received a question from a customer using the image inspection software EasyInspector, asking, "Can inverter fluorescent lights not be dimmed?" We inform customers using EasyInspector that "if there are no issues with inspections using indoor fluorescent lights or inverter fluorescent lights, there is no need to use expensive lighting." Many LED lights have separate lighting and power units, and since the power unit can be dimmed with a knob, I thought such questions were natural. In fact, there are various methods for adjusting brightness: 1) Dimming 2) Aperture of the lens (in the case of industrial cameras) 3) Camera exposure time and gain settings In other words, in principle, if any of the above three can be adjusted, brightness can be controlled. So, what is the best method to use for adjustment? (Figure 1) This is a photograph of part of a bottle cap. The focus is set on the bottom of the cap. Both images have the same brightness, but you can see that the left image has the focus extending to the edge of the cap compared to the right. This is referred to as "having a deep depth of field."

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One of the common concerns when selecting a camera is the interface. In recent years, most manufacturers of industrial cameras have released both GigE and USB3.0 cameras. This is because each of the two interfaces has its own advantages and disadvantages. In this article, I would like to discuss how to choose the appropriate interface based on your application. ■ Price Comparison The differences between the two are clearly defined in the specifications, so I will extract some of that information. By looking at the table below, you may clarify which camera you should choose for your current application. (Note: Manufacturers offer cables up to 5m or 8m, but there may be cases where operation becomes unstable.) ■ Power Supply for GigE Cameras When connecting to a PC via GigE, you cannot power the camera directly from the PC even if you connect the PC and camera with a LAN cable. Therefore, you need to either power the camera directly or use a separate PoE injector or PoE hub between the PC and the camera to supply power to the camera. (Note: PoE hub capable of powering 4 cameras (BS-GU2005P))

• Visual Inspection Equipment

The types of camera light sensors can be broadly divided into two categories: "global shutter" and "rolling shutter." The difference between these two lies in whether all the pixels of the sensor are exposed simultaneously (global shutter) or if they are exposed one line at a time from the top down (rolling shutter). How does this difference in mechanism affect the image? A typical example is the distortion shown below. When capturing a moving object, with a global shutter, a spherical object will be photographed as a sphere, but with a rolling shutter, because exposure occurs line by line from the top, the object may have moved during the exposure time, resulting in distortion. Thus, the differences between the two become apparent when photographing moving subjects. One might wonder, "Wouldn't it be better to use only global shutters?" However, in terms of manufacturing costs, rolling shutters are more advantageous (lower cost). (Figure 1: Left: Global Shutter, Right: Rolling Shutter)

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As explained in "Whether High Pixel or Low Pixel (2)," industrial cameras come with various sensor sizes. Generally, they range from 1/3 to 1/2 inch, but there are also sizes like 1 inch. A larger sensor size means that, with the same number of pixels, the pixel size increases, resulting in: - A larger light-receiving area per pixel, allowing for shorter exposure times. - Less noise in images when using the same exposure time. - Sharper images with the same lens focusing performance (it might be easier to understand if you think that a larger pixel size makes the focus target larger). These are the advantages (however, larger sensor sizes are more expensive to produce, so the price also increases accordingly). One point to note is that if you want to change the sensor size from 1/2 inch to 1 inch to improve image quality, the angle of view (field of vision) will also change, even if you use the same lens.

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In "(1) High Pixels or Low Pixels," we discussed the advantages of high-pixel cameras in capturing details clearly. Here, I would like to write about the advantages of low-pixel cameras. The advantage of low-pixel cameras can be summed up in one phrase: "processing is faster." There are two reasons for the faster processing. 1. There are fewer pixels to handle as image data. 2. The exposure time can be shorter, resulting in less time needed for shooting. I believe there is no room for doubt regarding point 1. So, why can the exposure time be shorter in point 2? Let me explain. The size of the camera sensor (the square light-receiving part shown in the initial photo) varies, but in industrial cameras, sizes around 5mm square to 7mm square are common (1/3 inch to 1/2 inch). However, as mentioned in "High Pixels or Low Pixels (1)," the variation in pixel counts ranges from about 300,000 pixels to around 14 million pixels. At this time, what differences exist in the size of the pixels?

• Visual Inspection Equipment

"High pixels mean high performance" — Indeed, as the pixel count increases, it becomes possible to capture details more clearly. However, it does not simply mean that larger is better, as high-pixel cameras also have drawbacks. For example, using a camera with unnecessarily high pixel counts can lead to longer image processing times. Therefore, it is necessary to consider whether "it is really impossible to make a judgment without using a high-pixel camera?" when selecting the pixel count of a camera. The example below shows the results of an experiment on how much difference in clarity there is when enlarging a part of a PC motherboard from 300,000 pixels to 14 million pixels. (Refer to the figure) In this way, high pixel performance is demonstrated when trying to see fine details within a wide field of view. However, as mentioned earlier, not only does image processing take longer, but as will be introduced in "High pixels or low pixels (2)," it is also necessary to increase the exposure time. The consideration of whether "it is really impossible to make a judgment without using a high-pixel camera?" is one of the important factors in selecting a camera.

• Visual Inspection Equipment

As a representative of inexpensive cameras, there are webcams that can be connected to a computer via USB. Their prices start from around 1,000 yen, making them quite affordable, but it's good to understand the reasons why industrial cameras are generally used for image processing to avoid potential issues. Of course, the cheaper option increases cost-effectiveness, but in my experience, there are "3" customers using webcams compared to "7" customers using industrial cameras. Conversely, this means that 30% of customers can operate effectively with a webcam.

• Visual Inspection Equipment

The aperture acts like the iris (brown eye) in the eye, adjusting the amount of light that enters the camera's image sensor, while also affecting the depth of field and the camera's exposure time. This time, I would like to examine depth of field and exposure time. ■Depth of Field ■Exposure Time ■How to Increase Depth of Field While Reducing Exposure Time

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Lenses that allow for adjustments in aperture and focus have a value called "focal length," which relates to the angle of view (shooting range). When expressing the specifications of a lens, it is common to represent the maximum aperture value and focal length together, such as f1.4/12mm, which indicates that "focal length" is an important specification. Here, we will introduce how to choose a focal length. *For more details, please see the related links.*

• Visual Inspection Equipment

When inspecting with images, the quality of the light that enters the camera's light sensor significantly affects whether the defects you want to detect can be found or not. 1: Influence of Reflected Light One of the factors that changes significantly depending on the angle of illumination is reflected light. For example, as shown in the example below, even when photographing the same object, the appearance can change completely depending on the angle of the lighting. (Figure 1: Left: When illuminated from directly above, Right: When illuminated from an angle) The sample above has a moisture-proof coating applied to the surface of the substrate, giving it an overall glossy appearance. When photographing such an object, if light is directed straight on, the light source may reflect directly, resulting in an undesirable image. In such cases, the lighting is angled. (Figure 2: Left: When illuminated from directly above, Right: When illuminated from an angle) The blue arrows in the above diagram indicate the reflection of the light source. When light is directed from directly above, the reflected light enters the camera directly, but when illuminated from an angle, the reflected light escapes to the opposite side, allowing you to avoid direct reflected light.

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I would like to write about various types and characteristics of lighting, as well as their main uses. Please choose the optimal lighting according to your needs. ■ Bar lighting ■ Ring lighting ■ Low-angle ring lighting ■ Backlight (transmitted light) ■ Backlight + polarizing filter ■ Coaxial illumination ■ Dome lighting *For more details, please refer to the related links.

• Visual Inspection Equipment

In the manufacturing industry, quality control and quality assurance are essential tasks for maintaining corporate trust. Among the various quality control tasks, "visual inspection" occupies a significant portion. By conducting visual inspections at each stage of the process, the yield of downstream processes can be improved, and the outflow of defects can be prevented in advance. However, this can also increase manufacturing costs and, in some cases, the visual inspection may become a bottleneck, leading to a decrease in production capacity. Here, we will discuss the points to consider for automating visual inspections, the differences between traditional image processing and the latest AI image processing, and trends in visual inspection overseas. ■ What is visual inspection? ■ Main inspection items of visual inspection ■ Automated visual inspection as an alternative to manual inspection ■ Advantages and disadvantages of manual inspection ■ Advantages and disadvantages of automated visual inspection ■ Steps for implementing automated visual inspection ■ Will AI (deep learning) visual inspection replace traditional rule-based image processing? ■ Situations where procedural image processing is used in visual inspection ■ Situations where AI is used in visual inspection ■ Points to consider when automating visual inspection ■ Trends in automated visual inspection overseas ■ Summary ■ Visual inspection systems that can be implemented at low cost *Please see the related links.

• Visual Inspection Equipment

We reported the presence or absence of tears in shrink-wrapped products from food manufacturers through a free simple verification process. We would like to continue verifying issues such as deformation, stacking collapse, and flap adhesion, and propose various defect detection methods. In the simple verification, we were able to accurately identify approximately 80% of tears that are easily noticeable by the human eye, and about 50% of tears that are less noticeable, such as those on white products. This time, we conducted the verification with a limited number of samples, and I believe the accuracy was affected by the small amount of training data. The detection accuracy will improve with more data for training. [Software Used] Deepsky

• Visual Inspection Equipment

We received inquiries from an electronic equipment manufacturer regarding the display lights and meter readings of various control devices. We have decided to set up and report on six types of reading functions. The two images on the left and right utilize the "meter reading function." For the other three images, we determine pass or fail based on whether the color is detected when lit or not. However, I believe there is variability in how the lighting hits the upper and lower parts of the meter, so I think that illuminating the lower part, which tends to be darker, with bar lighting or similar would reduce misdetections. Regarding the other image we received, reading was possible with the image you provided. Additionally, some minor adjustment for misalignment is possible, but fixing the camera is essential.

• Visual Inspection Equipment