Visual-based Defect Detection for Product Classification: A Machine Learning Approach

Mar 03 . 1min read

Visual inspection is the final stage for quality product testing of infrared detectors. Traditional methods for this type of work commonly rely on human visual inspection, which is the ultimate standard for determining the visual quality of the image. However, using human inspection can be problematic because of the statistical variation between observers. Automated methods based on machine learning techniques, and especially Deep Learning, have emerged in recent decades that are able to closely match the visual perception of the human eye, while providing the added benefits of speed and consistency.

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