Multi-level decoding neural network for pitting detection of ball screw
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.