MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
As the performance requirements for printed circuit boards (PCBs) in electronic devices continue to increase, reliable defect detection during PCB manufacturing is vital. However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6238 |
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| author | Xupeng Yin Zikai Zhao Liguo Weng |
| author_facet | Xupeng Yin Zikai Zhao Liguo Weng |
| author_sort | Xupeng Yin |
| collection | DOAJ |
| description | As the performance requirements for printed circuit boards (PCBs) in electronic devices continue to increase, reliable defect detection during PCB manufacturing is vital. However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight detection algorithm for PCB defect detection based on improved YOLOv12 architecture. In the Backbone, a Median-enhanced Channel and Spatial Attention Block (MECS) expands the receptive field through median enhancement and depthwise convolution to generate attention maps that effectively capture subtle defect features. In the Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features through weighted integration, enhancing feature utilization and focus on defect regions. Moreover, the original YOLOv12 loss function is replaced with the Slide Alignment Loss (SAL) to improve bounding box localization and detect complex defect types. Experimental results demonstrate that MAS-YOLO significantly improves mean average precision (mAP) and frames per second (FPS) compared to the original YOLOv12, fulfilling real-time industrial detection requirements. |
| format | Article |
| id | doaj-art-5c1f21435f8940ecb06857b1bc4844c8 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5c1f21435f8940ecb06857b1bc4844c82025-08-20T02:23:01ZengMDPI AGApplied Sciences2076-34172025-06-011511623810.3390/app15116238MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12Xupeng Yin0Zikai Zhao1Liguo Weng2Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaAs the performance requirements for printed circuit boards (PCBs) in electronic devices continue to increase, reliable defect detection during PCB manufacturing is vital. However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight detection algorithm for PCB defect detection based on improved YOLOv12 architecture. In the Backbone, a Median-enhanced Channel and Spatial Attention Block (MECS) expands the receptive field through median enhancement and depthwise convolution to generate attention maps that effectively capture subtle defect features. In the Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features through weighted integration, enhancing feature utilization and focus on defect regions. Moreover, the original YOLOv12 loss function is replaced with the Slide Alignment Loss (SAL) to improve bounding box localization and detect complex defect types. Experimental results demonstrate that MAS-YOLO significantly improves mean average precision (mAP) and frames per second (FPS) compared to the original YOLOv12, fulfilling real-time industrial detection requirements.https://www.mdpi.com/2076-3417/15/11/6238defect detectionsmall target detectionYOLOv12attention mechanismloss function |
| spellingShingle | Xupeng Yin Zikai Zhao Liguo Weng MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 Applied Sciences defect detection small target detection YOLOv12 attention mechanism loss function |
| title | MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 |
| title_full | MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 |
| title_fullStr | MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 |
| title_full_unstemmed | MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 |
| title_short | MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 |
| title_sort | mas yolo a lightweight detection algorithm for pcb defect detection based on improved yolov12 |
| topic | defect detection small target detection YOLOv12 attention mechanism loss function |
| url | https://www.mdpi.com/2076-3417/15/11/6238 |
| work_keys_str_mv | AT xupengyin masyoloalightweightdetectionalgorithmforpcbdefectdetectionbasedonimprovedyolov12 AT zikaizhao masyoloalightweightdetectionalgorithmforpcbdefectdetectionbasedonimprovedyolov12 AT liguoweng masyoloalightweightdetectionalgorithmforpcbdefectdetectionbasedonimprovedyolov12 |