天津工业大学学报2024，Vol.43Issue(3)：50-57,8.DOI:10.3969/j.issn.1671-024x.2024.03.007
基于RT-YOLO-V5的芯片外观缺陷检测
Chip appearance defect detection based on RT-YOLO-V5
摘要
Abstract
Aiming at the problems caused by traditional manual chip detection,with low efficiency,excessive dependence on human operation and high misdetection rate,an RT-YOLO-V5 detection method was proposed to detect chip appearance defects based on the Res-CBS module and an additional Tiny-scale detection layer.First of all,an image acquisition system was built,and a chip appearance defect detection dataset was produced.Due to the de-fects are irregular in shape,inconsistent in size and uncertain in location,the performance of YOLO-V5 network can no longer meet the detection requirements.A short connection was added to the CBS module,fusing the fea-ture information of input and output,reducing the information loss and optimizing the inference speed.In addi-tion,a tiny-scale detection layer is added as well,to improve the feature extraction capability of the model for tiny targets.The experimental results show that using the improved network for chip appearance defect detection,mAP reached 95.5％,which was a 5.7％improvement compared to the original network.In addition,the im-proved RT-YOLO-V5 has gained some improvement in both Box_loss and the accuracy of tiny-scale defect de-tection.关键词
YOLO-V5/芯片/缺陷检测/特征融合/卷积神经网络Key words
YOLO-V5/chip/defect detecting/feature fusion/convolutional neural network分类
信息技术与安全科学引用本文复制引用
郭翠娟,王妍,刘净月,席雨,徐伟,王坦..基于RT-YOLO-V5的芯片外观缺陷检测[J].天津工业大学学报,2024,43(3):50-57,8.基金项目
中国博士后科学基金面上基金资助项目(2019M661013) （2019M661013）
天津市科技计划资助项目(20YDTPJC01090 （）
22YDTPJC00090) （）
