Due to the small pitting area of the ball screw and the serious environmental interference,defects are difficult to detect in time.Therefore,a Multi-level decoding neural network is proposed to realize the segmentation of pitting defects in ball screws.The network consists of an encoder,a multi-level decoder and a Multi-scale Attention module.The encoder is composed of Resnet34,and the Ghost module is introduced to build a lightweight multi-level decoder.In order to fuse multi-scale features and filter redundant information,the Multi-scale Attention module is designed.A hybrid loss function composed of BCE function,IOU and SSIM function is used to train the network.Experiments on the ball screw defect dataset show that Multi-level decoding neural network achieves 0.770 3 in the maxFβ metrics,compared with other methods,which achieves better segmentation results,and the processing time of a single image is 26 ms.It provides a new method for real-time segmentation of ball screw pitting defects.
维普
万方数据