Inspection of unexpected defective products by semi-supervised learning based on a probability density function in high-yield food production

In this research, we propose a method for evaluating images to be inspected using only good images and assuming defective images are unavailable in anticipation of quality inspection applications in food processing facilities and other factories. We propose a discriminator based on a CNN that can evaluate the degree of deviation from good images by treating only good images as training data and assuming a fitness probability distribution. The results show that the proposed discriminator can detect defective products even without prior training data on defective products. The detection accuracy depends on the inspected object and the threshold that defines the deviation, which is comparable to previous studies that require defective images. With adjustable detection thresholds and automatic categorization of defective products, the proposed method is expected to be flexible enough to incorporate the knowledge of shop-floor workers on the production line.