Tube Defect Detection Algorithm Under Noisy Environment Using Feature Vector and Neural Networks

Surface flaw detection has been advanced steadily for decades thank to the advent of computer vision and artificial intelligence. However, there exist serious defect detection challenges in tube manufacturing, including the lack of a collected dataset, decision-making ambiguity in engineering judgment, and unstable lighting condition of the environment. This work aims to investigate an effective method to distinguish deformity that performs despite these challenges to deliver quality control in tube manufacturing. We present a new tube detection algorithm under limited data set and noisy environment due to unstable lighting condition, for which we introduced a feature vector to describe the defect problem. Using the feature vector and a neural network we are able to successfully detect and classify tube defect.