Research on Workpiece Detection with Hole Based on Improved Multi-scale Faster-RCNN
Aiming at the problems of fuzzy classification and low efficiency of defect location in traditional machine vision and image processing,an improved multi-scale Faster-RCNN method for defect detection of workpiece with holes was proposed.Firstly,the image acquisition and preprocessing of perforated workpiece are carried out,and the data enhancement method is improved to ensure the quality of the data training set.Secondly,the Faster-RCNN network is built as the basic framework,and ResNet50 as the main feature extraction network is used to improve the internal residual structure and strengthen the RPN network to extract multi-scale feature map data.Then,by improving the original non-maximum suppression algorithm,Soft-NMS algorithm is used to classify the dense holes and defect features respectively.Finally,a comparative experiment is carried out to compare the Faster-RCNN algorithm with the improved multi-scale algorithm.The results show that the improved multi-scale Faster-RCNN can achieve 92%identification accuracy for workpiece hole features and defects,and the average accuracy is improved by 4.35%compared with the original algorithm.It can simultaneously identify workpiece hole features and nearby defects,and the neural network has high robust adaptability.
万方数据
维普
