PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure
Printed circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3550 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161034401153024 |
|---|---|
| author | Youzhi Xu Hao Wu Yulong Liu Xing Zhang |
| author_facet | Youzhi Xu Hao Wu Yulong Liu Xing Zhang |
| author_sort | Youzhi Xu |
| collection | DOAJ |
| description | Printed circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a large number of parameters and the runtime is too long. The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. To solve this problem, we innovated and modified the YOLO11n model. Firstly, we used the Retention Block (RetBlock) to improve the C3K2 module in the backbone, creating the RetC3K2 module, which makes up for the limitation of the original module’s limited, purely convolutional local receptive field. Secondly, the neck structure of the original model network is fused with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) structure and turned into a multi-branch auxiliary neck network, which enhances the model’s ability to fuse multiple scaled characteristics and conveys diverse information about the gradient for the output layer. The improved YOLO11n model improves its mAP50 by 0.023 (2.5%) and mAP75 by 0.026 (2.8%) in comparison with the primitive model network, and detection precision is significantly improved, proving the superiority of our proposed approach. |
| format | Article |
| id | doaj-art-c2d30a28d23d4832b036953a5fce23e1 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c2d30a28d23d4832b036953a5fce23e12025-08-20T02:22:59ZengMDPI AGSensors1424-82202025-06-012511355010.3390/s25113550PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck StructureYouzhi Xu0Hao Wu1Yulong Liu2Xing Zhang3School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, ChinaPrinted circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a large number of parameters and the runtime is too long. The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. To solve this problem, we innovated and modified the YOLO11n model. Firstly, we used the Retention Block (RetBlock) to improve the C3K2 module in the backbone, creating the RetC3K2 module, which makes up for the limitation of the original module’s limited, purely convolutional local receptive field. Secondly, the neck structure of the original model network is fused with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) structure and turned into a multi-branch auxiliary neck network, which enhances the model’s ability to fuse multiple scaled characteristics and conveys diverse information about the gradient for the output layer. The improved YOLO11n model improves its mAP50 by 0.023 (2.5%) and mAP75 by 0.026 (2.8%) in comparison with the primitive model network, and detection precision is significantly improved, proving the superiority of our proposed approach.https://www.mdpi.com/1424-8220/25/11/3550PCBdefect detectionYOLO11nRetBlockMAFPN |
| spellingShingle | Youzhi Xu Hao Wu Yulong Liu Xing Zhang PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure Sensors PCB defect detection YOLO11n RetBlock MAFPN |
| title | PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure |
| title_full | PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure |
| title_fullStr | PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure |
| title_full_unstemmed | PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure |
| title_short | PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure |
| title_sort | pcb electronic component soldering defect detection using yolo11 improved by retention block and neck structure |
| topic | PCB defect detection YOLO11n RetBlock MAFPN |
| url | https://www.mdpi.com/1424-8220/25/11/3550 |
| work_keys_str_mv | AT youzhixu pcbelectroniccomponentsolderingdefectdetectionusingyolo11improvedbyretentionblockandneckstructure AT haowu pcbelectroniccomponentsolderingdefectdetectionusingyolo11improvedbyretentionblockandneckstructure AT yulongliu pcbelectroniccomponentsolderingdefectdetectionusingyolo11improvedbyretentionblockandneckstructure AT xingzhang pcbelectroniccomponentsolderingdefectdetectionusingyolo11improvedbyretentionblockandneckstructure |