How to reduce false negatives in image anomaly detection?

Question

I'm currently working in a quality inspection project and I need to develop a program that can detect irregular parts. The problem I'm facing is that I don't have many irregular samples (only seven for more than 3,000 regular ones). I tried with CNN's but due to the unbalanced number of samples the model detects all as regular, so the approach I'm exploring is to use anomaly detection algorithms. I also tried with autoencoders but as the differences between regular and irregular are minimal, I could't get any good results. So far, the approach that gave me the best results is with Local Outlier Factor in combination with feature extractors (HOG). The only problem with this one is that even after tuning the algorithm's parameters it still gives me false positives (normal samples are labeled as irregular), which for this application is not acceptable. Is there anything I can add to the process to eliminate the false positives? o can you recommend me other approach? I'd really appreciate any help :)

Answer 1

Use Focal loss function since u have imbalanced data or u can try data augmentation technique as well.