PCB Defect Detection and Instance Segmentation Algorithm Based on TAC-YOLOv11s
WANG Xinlu
ZHENG Xiaoliang
LAI Wenhao
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.
Keywords：printed circuit boarddefect detectioninstance segmentationYOLOv11ssmall target
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 80-85 )