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题名FDDC-YOLO: an efficient detection algorithm for dense small-target solder joint defects in PCB inspection
作者
发表日期2025-04-01
发表期刊Journal of Real-Time Image Processing
ISSN/eISSN1861-8200
卷号22期号:2
摘要

Nowadays printed circuit board plays a vital role in communication, computer, electronics and other industries. Existing PCB welding defect detection algorithms have the problems of low accuracy and poor real-time performance in identifying small or irregular targets and dense solder joints. This is due to the limited receptive field of standard convolutional kernels, which hinder global feature extraction and focus on local details. Moreover, the effects of kernel count and feature extraction dimensions are often overlooked, leading to the loss of important features. Conventional upsampling methods, such as nearest-neighbor interpolation, can further degrade critical information. To address these challenges, we propose FDDC-YOLO, a novel defect detection network. First, we introduce a new full-dimensional dynamic convolution module FDDC, which integrates full-dimensional dynamic convolution with the newly designed od_ottleneck structure to enhance the feature extraction ability by using the four dimensions of the convolution kernel. Secondly, the CECA attention module in the neck improves the ability of the model to detect small defects by enhancing the local interaction between channels. Third, the Dy-Up module is used to improve image resolution and prevent the loss of detailed information during the detection process. Finally, we replace the CIoU loss with IShapeIoU to reduce the overlap of detection boxes in densely packed solder joints, improving both localization accuracy and convergence speed.The mAP of FDDC-YOLO is improved by 5.4% on the PCBSP_dataset, and a Frame Per Second (FPS) of 189. It improves by 3.8% on the public PCB Defect-Augmented dataset, which proves its good generalization ability.

关键词Attention module Dense solder joints Dynamic convolution PCB soldering detection Real-time
DOI10.1007/s11554-025-01664-4
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收录类别SCIE
语种英语English
WOS研究方向Computer Science ; EngineeringImaging Science & Photographic Technology
WOS类目Computer Science, Artificial IntelligenceEngineering, Electrical & ElectronicI maging Science & Photographic Technology
WOS记录号WOS:001446582900001
Scopus入藏号2-s2.0-105000376540
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/12809
专题北师香港浸会大学
通讯作者Peng, Jinmin
作者单位
1.Fujian Key Laboratory of Intelligent Processing Technology and Equipment (Fujian University of Technology),Fuzhou,350118,China
2.School of Mechanical and Automotive Engineering,Fujian University of Technology,Fuzhou,350118,China
3.Fulongma Group Co.,Ltd,Longyan,364028,China
4.Faculty of Science and Technology,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,519087,China
推荐引用方式
GB/T 7714
Zheng, Haoyu,Peng, Jinmin,Yu, Xinyiet al. FDDC-YOLO: an efficient detection algorithm for dense small-target solder joint defects in PCB inspection[J]. Journal of Real-Time Image Processing, 2025, 22(2).
APA Zheng, Haoyu, Peng, Jinmin, Yu, Xinyi, Wu, Meishun, Huang, Qiufang, & Chen, Liangshen. (2025). FDDC-YOLO: an efficient detection algorithm for dense small-target solder joint defects in PCB inspection. Journal of Real-Time Image Processing, 22(2).
MLA Zheng, Haoyu,et al."FDDC-YOLO: an efficient detection algorithm for dense small-target solder joint defects in PCB inspection". Journal of Real-Time Image Processing 22.2(2025).
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