Detail extraction of mechanical component image based on depth learning

Abstract

Abstract Aiming at the problem of feature extraction, recognition and classification of machining features of various mechanical part models, an image detail extraction method based on deep learning architecture is proposed. We use the median filtering method to filter the impulse noise in the image initially. After horizontal tilt correction, the contour image is divided into several fan sub regions. Then, the structural parameters of YOLOV3 network are optimized to make it more suitable for small target classification detection of industrial parts on the basis of fully retaining the feature extraction ability of depth convolution network. The simulation results show that for original YOLOv3 algorithm, the improved network model has good robustness, and the problems of different scales, low feature utilization and occlusion in part detection which are obviously improved. Compared with the traditional methods of feature extraction and classification of mechanical part images, the deep learning strategy also shows greater advantages.