湖北民族大学学报（自然科学版）2025，Vol.43Issue(1)：80-85,6.DOI:10.13501/j.cnki.42-1908/n.2025.03.018
基于TAC-YOLOv11s的PCB缺陷检测与实例分割算法
PCB Defect Detection and Instance Segmentation Algorithm Based on TAC-YOLOv11s
摘要
Abstract
To address the issue of low recognition accuracy caused by the small size of targets in printed circuit board(PCB)defects,a detection and instance segmentation algorithm based on triplet attention and cross stage connections-you only look once version 11 small(TAC-YOLOv11s)was proposed.Firstly,a cross stage partial connections(CSPC)feature extraction network was designed to enhance the network′s feature representation capability.Secondly,a small object segmentation head(SO)module was added to improve the detection and segmentation ability for small objects.Thirdly,a triplet attention(TA)mechanism was incorporated to increase the localization and capture of small targets.Lastly,generalized intersection over union(GIoU)loss function was adopted to optimize the performance of the algorithm.The results demonstrated that the TAC-YOLOv11s algorithm improved by 11.1%and 8.2%in bounding box and mask precision,respectively,and the mean average precision with an intersection over union threshold of 50%for bounding boxes and masks increased by 30.4%and 34.3%,respectively,compared to the original YOLOv11s algorithm,thoroughly validating the superiority of this algorithm.TAC-YOLOv11s algorithm signified its importance in achieving high-precision detection and segmentation of PCB defects.关键词
印刷电路板/缺陷检测/实例分割/YOLOv11s/小目标Key words
printed circuit board/defect detection/instance segmentation/YOLOv11s/small target分类
信息技术与安全科学引用本文复制引用
王欣璐,郑晓亮,来文豪..基于TAC-YOLOv11s的PCB缺陷检测与实例分割算法[J].湖北民族大学学报（自然科学版）,2025,43(1):80-85,6.基金项目
安徽理工大学高层次引进人才科研启动基金资助项目(2021yjrc02). （2021yjrc02）
