Defect detection on complex texture surface based on optimized ResNet

Defect detection of product surface is an important part of industrial automatic production and the accuracy is the main index to evaluate the reliability of automatic detection system. Based on the particularity of defect detection on complex texture surface and the requirements of real-time and universal detection methods, a detection method for optimizing the backbone network and using the transfer learning feature mapping to construct the complex texture surface defects was proposed. In this method, the ResNet model was optimized and the simulation data set was established for transfer learning, so as to solve the problems such as the small number of samples in the data set of complex texture surface products, the difficulty of data set making, and the difficulty of similar problems to be compatible with each other. Experimental results show that the proposed method can accurately detect the surface defects of artificial wooden plank with random complex texture, and the average accuracy can reach 99.6%. Under the existing experimental conditions, the detection time of a single artificial wooden plank is 305 ms, which can meet the real-time requirements of online detection. The research results can provide a new idea and theoretical reference for the defect detection on complex texture surface based on deep learning.