改进Faster RCNN的瓷砖表面瑕疵检测研究 赵楚.pdf

: .
计算机工程与P 为 %, 比原始 Faster RCNN 算法提高了 %，可以更好地检测小目标瑕
疵，满足瓷砖表面瑕疵检测的要求。
关键词：目标检测; 瓷砖表面瑕疵; Faster RCNN; Rank & Sort Loss; 可变形卷积
文献标志码: A 中图分类号:TP391 doi：.1002--0414
Research On Ceramic Tile Surface Defect Detection By Improved Faster RCNN
ZHAO Chu, DUAN Xianhua, SU Junkai
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212100, China

Abstract：Aiming at the problems of minimal defect target, large difference of defect shape, easy missing detection
and low accuracy in ceramic tile surface defects, an improved ceramic tile surface defect detection algorithm based
on Faster RCNN is proposed. Firstly, based on the original Faster RCNN, resnet101 is selected as the feature ex-
traction network, and deformable convolution networks is introduced in the last three stages of resnet101 to adap-
tively learn the defect features. Secondly, the regional proposals network is optimized, and the anchor generation
parameters are improved through the analysis of ceramic tile data set, so that the generated anchors are more con-
sistent with the target scale and the positioning is more accurate. Finally, the loss function is optimized and Rank
& Sort loss is introduced to reduce the number of super parameters and improve the performance of the model,
making it more robust to the class imbalance problem in training. Experimental results show that the average de-
tection accuracy of the improved Faster RCNN is %, which is % higher than that of Faster RCNN. It can
better detect small target defects and meet the requirements of ceramic tile surface defect detection.
Key words：target detection; ceramic tile surface defect; Faster RCNN; Rank & Sort Lo