[AI Image Inspection Case] Size of Annotation

I will share the key points for "annotation" to teach the AI where to detect.

This is a support case for a customer who has actually implemented DeepSky. We provided guidance to increase the detection rate. The "annotation" used to teach the AI where to detect is crucial for inspection accuracy. The image below shows annotations that had a low detection rate. We advised that annotations that are too large or too small decrease the detection rate. Our company offers verification and support from technical staff on a daily basis. If you have any issues, questions, or uncertainties during operation, please feel free to contact us at any time. 【Inspection Settings and Results】 We set the judgment to "fail" after detecting one or more defective areas. (The inspected images were unknown images different from the trained teacher images.) We inspected 32 unknown images (16 good products / 16 defective products) and achieved 30 correct judgments and 2 incorrect judgments (we failed to detect defects, and all good products were correctly judged). *We also inspected the teacher images, but there were 2 false detections. It is likely that increasing the number of training images and the training time will improve detection. 【Software Used】 Software used: DeepSky

Image Processing Software