发表状态 | 已发表Published |
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
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ISSN/eISSN | 1861-8200 |
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 |
DOI | 10.1007/s11554-025-01664-4 |
URL | 查看来源 |
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 | |
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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