MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 DOI Creative Commons
Xi Yin, Zikai Zhao, Liguo Weng

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(11), С. 6238 - 6238

Опубликована: Июнь 1, 2025

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 small size, complex categories, and subtle differences features, traditional methods are limited accuracy robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight algorithm based on improved YOLOv12 architecture. In Backbone, Median-enhanced Channel Spatial Attention Block (MECS) expands receptive field through median enhancement depthwise convolution generate attention maps that effectively capture features. Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features weighted integration, enhancing feature utilization focus regions. Moreover, original loss function replaced with Slide Alignment Loss (SAL) improve bounding box localization detect types. Experimental results demonstrate MAS-YOLO significantly improves mean average precision (mAP) frames per second (FPS) compared YOLOv12, fulfilling real-time industrial requirements.

Язык: Английский

CV-YOLOv10-AR-M: Foreign Object Detection in Pu-Erh Tea Based on Five-Fold Cross-Validation DOI Creative Commons
Wenxia Yuan, Chunhua Yang, Xinghua Wang

и другие.

Foods, Год журнала: 2025, Номер 14(10), С. 1680 - 1680

Опубликована: Май 9, 2025

To address the problem of detecting foreign bodies in Pu-erh tea, this study proposes an intelligent detection method based on improved YOLOv10 network. By introducing MPDIoU loss function, network is optimized to effectively enhance positioning accuracy model complex background and improve small target objects. Using AssemFormer optimize structure, network’s ability perceive objects its process global information are improved. Rectangular Self-Calibrated Module, prediction bounding box optimized, further improving classification target-positioning abilities scenes. The results showed that Box, Cls, Dfl functions CV-YOLOv10-AR-M One-to-Many Head task were, respectively, 14.60%, 19.74%, 20.15% lower than those In One-to-One task, they decreased by 10.42%, 29.11%, 20.15%, respectively. Compared with original network, accuracy, recall rate, mAP were increased 5.35%, 11.72% 8.32%, improves model’s attention sizes, backgrounds, detailed information, providing effective technical support for quality control agricultural field.

Язык: Английский

Процитировано

0
MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 DOI Creative Commons
Xi Yin, Zikai Zhao, Liguo Weng

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(11), С. 6238 - 6238

Опубликована: Июнь 1, 2025

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 small size, complex categories, and subtle differences features, traditional methods are limited accuracy robustness. To overcome these challenges, this paper proposes MAS-YOLO, a lightweight algorithm based on improved YOLOv12 architecture. In Backbone, Median-enhanced Channel Spatial Attention Block (MECS) expands receptive field through median enhancement depthwise convolution generate attention maps that effectively capture features. Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) adaptively fuses multi-scale features weighted integration, enhancing feature utilization focus regions. Moreover, original loss function replaced with Slide Alignment Loss (SAL) improve bounding box localization detect types. Experimental results demonstrate MAS-YOLO significantly improves mean average precision (mAP) frames per second (FPS) compared YOLOv12, fulfilling real-time industrial requirements.

Язык: Английский

Процитировано

0