Casting X-ray image segmentation based on deep learning

Abstract

Aiming at the problems that the current image segmentation algorithm has low accuracy in realizing the internal defect segmentation of industrial castings and the algorithm is not lightweight enough, an improved industrial casting internal defect detection algorithm Effi-DeepLab based on DeepLabv3+ was proposed. This method used MBConv in EfficientNet to replace the original Xception module for feature extraction, making the feature extraction network more efficient and lighter. Aiming at the problem of small internal defects in industrial castings, the expansion rate of the hollow convolution in the atrous spatial pyramid pooling (ASPP) layer was redesigned. The convolution block was more robust to small targets; the low-order semantic information in the feature extraction stage was fully utilized at the decoding end to perform multi-scale feature fusion to improve the accuracy of small target defect segmentation. The experimental results show that the segmentation accuracy and mIoU of the Effi-DeepLab model in the internal defect image data set of the automobile wheel are 93.58% and 89.39%, respectively, which are improved by 2.65% and 2.24%, respectively, compared with DeepLabv3+, and has better segmentation effect. In addition, it is experimentally verified that the proposed algorithm has good generalization.