关键词: 偏光片, 缺陷检测, SPP-Net模块, Dense Block模块, YOLOv3-Tiny-D算法

Abstract: In recent years,there are strict requirements on polarizer’s production quality with its application more widely.The target detection algorithm of deep learning was adopted to detect three kinds of defects of polarized,which solved the problems of low detection accuracy and high hardware cost and optimize the production process.Based on YOLOV3-Tiny algorithm,Dense Block module and SPP-Net module were used to optimize the feature extraction network,and combines with the actual situation of the target to be detected to adjust the optimized network detection module,thus an improved algorithm YOLOV3-Tiny-D was proposed.The experiment showed that when the method was tested on polaroid data set,the detection accuracy of three kinds of defects namely dirt,scratch and mark were 90.74%,98.90% and 97.52% respectively,while the detection speed was guaranteed at the same time,and the average accuracy was 95.72%,7% higher than the original algorithm.

Key words: polarizer, defect detection, SPP-Net module, Dense Block module, YOLOv3-Tiny-D algorithm