What is the best approach for detecting the defect? Assuming that we have 10 same objects, they are lined up and equidistant. If any of them is rotated a very small angle(5 - 10deg), what is best method to detect them? I am using a camera to capture the image. I want to divide the image into 10 parts and then feed each part into CNN model to classify the defect. Is that a possible way? What is the recommendation for this problem?
It is a possible way, but this class of problems falls under outlier detection in machine learning.
The defining feature is that one can only train for usual events not rare events, there are few or even no data at all, so a different approach needs to be taken.
Since usual neural network training requires adequate samples from each class, a workaround is needed. For example autoencoder networks are an option for outlier detection