PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion
Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module repla...
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MDPI AG
2025-07-01
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| author | Heqi Yang Junming Dong Cancan Wang Zhida Lian Hui Chang |
| author_facet | Heqi Yang Junming Dong Cancan Wang Zhida Lian Hui Chang |
| author_sort | Heqi Yang |
| collection | DOAJ |
| description | Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. The second key innovation lies in the creation of the Efficient Feature Fusion and Aggregation Network (EFAN), which integrates a lightweight Spatial-Channel Decoupled Downsampling (SCDown) module and three innovative fusion pathways. This achieves substantial parameter reduction while effectively integrating shallow detail features with deep semantic features, preserving critical defect information across different feature levels. Finally, the Shape-IoU loss function is incorporated, focusing on bounding box shape and scale for more accurate regression and enhanced defect localization precision. Experiments on the enhanced Peking University PCB defect dataset show that PCES-YOLO achieves a mAP50 of 97.3% and a mAP50–95 of 77.2%. Compared to YOLOv11n, it shows improvements of 3.6% in mAP50 and 15.2% in mAP50–95. When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50–95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. These results indicate that PCES-YOLO outperforms these algorithms in terms of detection accuracy and efficiency, making it a promising high-precision and efficient solution for PCB defect detection in industrial settings. |
| format | Article |
| id | doaj-art-e7f8023165574ebcb7fdb94cd73fc0e6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e7f8023165574ebcb7fdb94cd73fc0e62025-08-20T03:50:16ZengMDPI AGApplied Sciences2076-34172025-07-011513758810.3390/app15137588PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature FusionHeqi Yang0Junming Dong1Cancan Wang2Zhida Lian3Hui Chang4School of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaPrinted circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. The second key innovation lies in the creation of the Efficient Feature Fusion and Aggregation Network (EFAN), which integrates a lightweight Spatial-Channel Decoupled Downsampling (SCDown) module and three innovative fusion pathways. This achieves substantial parameter reduction while effectively integrating shallow detail features with deep semantic features, preserving critical defect information across different feature levels. Finally, the Shape-IoU loss function is incorporated, focusing on bounding box shape and scale for more accurate regression and enhanced defect localization precision. Experiments on the enhanced Peking University PCB defect dataset show that PCES-YOLO achieves a mAP50 of 97.3% and a mAP50–95 of 77.2%. Compared to YOLOv11n, it shows improvements of 3.6% in mAP50 and 15.2% in mAP50–95. When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50–95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. These results indicate that PCES-YOLO outperforms these algorithms in terms of detection accuracy and efficiency, making it a promising high-precision and efficient solution for PCB defect detection in industrial settings.https://www.mdpi.com/2076-3417/15/13/7588PCB defect detectionPCES-YOLOPre-convolution Receptive Field Enhancementfeature fusionloss function |
| spellingShingle | Heqi Yang Junming Dong Cancan Wang Zhida Lian Hui Chang PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion Applied Sciences PCB defect detection PCES-YOLO Pre-convolution Receptive Field Enhancement feature fusion loss function |
| title | PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion |
| title_full | PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion |
| title_fullStr | PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion |
| title_full_unstemmed | PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion |
| title_short | PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion |
| title_sort | pces yolo high precision pcb detection via pre convolution receptive field enhancement and geometry perception feature fusion |
| topic | PCB defect detection PCES-YOLO Pre-convolution Receptive Field Enhancement feature fusion loss function |
| url | https://www.mdpi.com/2076-3417/15/13/7588 |
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