Detection of Pitting Defects on the Surface of Ball Screw Drive Based on Improved Deeplabv3+Algorithm
Aiming at the problems of complex background environments,small pitting defect targets,and difficulty in detection on the surface of ball screw drives,an improved Deeplabv3+algorithm for segmenting surface defects of ball screw drives is pro-posed.This algorithm adopts Re2Net-50 to replace the backbone network of Deeplabv3+,significantly enhances the ability to recognize small-sized defect targets.Additionally,by integrating feature pyramid networks(FPN)into the backbone network,the algorithm effectively extracts multi-scale information,thereby improving the precise localization of defect targets.Finally,the coordinate attention mechanism is introduced after the ASPP module of the Deeplabv3+network,enhancing the model's focus on spatial dimensions within the image and effectively capturing long-range spatial dependencies.Experimental results demon-strate that,compared to the original Deeplabv3+,the proposed algorithm shows a 4.38％improvement in the mean intersection over union(MIoU)metric,a 5.52％increase in accuracy,and a 2.74％rise in F1-score.Furthermore,when compared with other classic semantic segmentation algorithms,the proposedalgorithm also exhibits certain superiority.
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