Algorithm for real-time defect detection of micro pipe inner surface

Quantitative analysis and identification of unknown shaped defects have always been difficult and challenging in the quality control of micro pipes. A series of algorithms for defect detection and feature recognition is presented in this study. A lightweight convolution neural network (LCNN) is introduced to realize defect discrimination. A shallow segmentation network is employed to cooperate with LCNN to obtain pixel-wise crack detection, and a feature recognition algorithm for quantitative measurement is presented. The experimental results show that the proposed algorithms can achieve defect detection with an accuracy of 98.5%, segmentation with mean intersection over union of 0.834, and latency of only 0.2 s. It can be used for online feature recognition and defect detection of the inner surface of a hole.