AI anomaly detection that learns only from good product images | No defective samples needed
DeepAge Sensor
Learn the standard of "normal" from good product images and detect those that deviate. It can also identify defects that have not been seen before. CPU operation.
In processes where defects rarely occur, it is not possible to collect defective product samples. The DeepAge Sensor's unsupervised anomaly detection learns the standard of "normal" using only good product images and detects images that deviate from that standard. It does not ask for "hundreds of defective product images." You can start inspection operations from the early stages of implementation, and it can also identify defects that have never been seen before as "deviating from normal." Inference operates on the CPU (no GPU required), and integration into the device is done with just one DLL. Later, if defective product images accumulate on-site, you can transition to a supervised model with the same API structure. In processes where samples are insufficient, you can first verify accuracy with free model creation.
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basic information
【Learning Method】Unsupervised anomaly detection (learning normal standards using only good images) 【Detection Principle】Detecting images that deviate from normal with an anomaly score (capable of handling unseen defects) 【Inference Speed】CPU 12ms/image (no GPU required, ONNX Runtime included) 【Model Format】.uai (incorporates judgment threshold within the model, no threshold management needed on the device side) 【Visualization】Displays "where it deviated from normal" with a contribution heatmap 【Transition】Once defective images are collected, transition to a supervised model (API structure remains the same)
Price information
Direct sales: 250,000 yen/camera/year~ / First model creation free
Price range
P3
Delivery Time
P3
※The initial model creation is free (accuracy report in as little as one week).
Applications/Examples of results
- Launch of visual inspection in a process with rare defects (no defective product samples needed) - Initial phase is unsupervised, transitioning to supervised in a gradual operation - Detection of unknown types of defects as "deviations"
Detailed information
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8 years of operational performance and key specifications in the manufacturing site.
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Unsupervised anomaly detection learning "normal" only from good product images.
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Learning is in the cloud, inference is on-site with CPU (no GPU needed).
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Send only good product images → Learning → Receive DLL → Integration (first time free)
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Applicable to target products in 6 industries (as deviation detection)
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Company information
We provide services that utilize data. The value generated from data is increasing day by day. While there may be a focus on numbers in sales targets and marketing, the importance of processing large amounts of data with computers to create new value has not been emphasized as much. Technology is evolving every day. We are now able to gather various data in the cloud and process large amounts of data. However, even with an understanding of such technologies and their value, practical application is still not sufficiently realized. We will accurately understand the current issues, sincerely consider whether artificial intelligence and new technologies can solve them, and communicate our findings to the world in the most optimal way.
