YOLOv8-DEE: a high-precision model for printed circuit board defect detection
Defects in printed circuit boards (PCBs) occurring during the production process of consumer electronic products can have a substantial impact on product quality, compromising both stability and reliability. Despite considerable efforts in PCB defect inspection, current detection models struggle wit...
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| Format: | Article |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2548.pdf |
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| author | Feifan Yi Ahmad Sufril Azlan Mohamed Mohd Halim Mohd Noor Fakhrozi Che Ani Zol Effendi Zolkefli |
| author_facet | Feifan Yi Ahmad Sufril Azlan Mohamed Mohd Halim Mohd Noor Fakhrozi Che Ani Zol Effendi Zolkefli |
| author_sort | Feifan Yi |
| collection | DOAJ |
| description | Defects in printed circuit boards (PCBs) occurring during the production process of consumer electronic products can have a substantial impact on product quality, compromising both stability and reliability. Despite considerable efforts in PCB defect inspection, current detection models struggle with accuracy due to complex backgrounds and multi-scale characteristics of PCB defects. This article introduces a novel network, YOLOv8-DSC-EMA-EIoU (YOLOv8-DEE), to address these challenges by enhancing the YOLOv8-L model. Firstly, an improved backbone network incorporating depthwise separable convolution (DSC) modules is designed to enhance the network’s ability to extract PCB defect features. Secondly, an efficient multi-scale attention (EMA) module is introduced in the network’s neck to improve contextual information interaction within complex PCB images. Lastly, the original complete intersection over union (CIoU) is replaced with efficient intersection over union (EIoU) to better highlight defect locations and accommodate varying sizes and aspect ratios, thereby enhancing detection accuracy. Experimental results show that YOLOv8-DEE achieves a mean average precision (mAP) of 97.5% and 98.7% on the HRIPCB and DeepPCB datasets, respectively, improving by 2.5% and 0.7% compared to YOLOv8-L. Additionally, YOLOv8-DEE outperforms other state-of-the-art methods in defect detection, demonstrating significant improvements in detecting small, medium, and large PCB defects. |
| format | Article |
| id | doaj-art-55461cb4dc97494b9594805c849e5e0c |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-55461cb4dc97494b9594805c849e5e0c2025-08-20T02:37:46ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e254810.7717/peerj-cs.2548YOLOv8-DEE: a high-precision model for printed circuit board defect detectionFeifan Yi0Ahmad Sufril Azlan Mohamed1Mohd Halim Mohd Noor2Fakhrozi Che Ani3Zol Effendi Zolkefli4School of Computer Sciences, Universiti Sains Malaysia, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Manufacturing Engineering, Western Digital SanDisk Storage Malaysia, Penang, MalaysiaDepartment of Manufacturing Engineering, Western Digital SanDisk Storage Malaysia, Penang, MalaysiaDefects in printed circuit boards (PCBs) occurring during the production process of consumer electronic products can have a substantial impact on product quality, compromising both stability and reliability. Despite considerable efforts in PCB defect inspection, current detection models struggle with accuracy due to complex backgrounds and multi-scale characteristics of PCB defects. This article introduces a novel network, YOLOv8-DSC-EMA-EIoU (YOLOv8-DEE), to address these challenges by enhancing the YOLOv8-L model. Firstly, an improved backbone network incorporating depthwise separable convolution (DSC) modules is designed to enhance the network’s ability to extract PCB defect features. Secondly, an efficient multi-scale attention (EMA) module is introduced in the network’s neck to improve contextual information interaction within complex PCB images. Lastly, the original complete intersection over union (CIoU) is replaced with efficient intersection over union (EIoU) to better highlight defect locations and accommodate varying sizes and aspect ratios, thereby enhancing detection accuracy. Experimental results show that YOLOv8-DEE achieves a mean average precision (mAP) of 97.5% and 98.7% on the HRIPCB and DeepPCB datasets, respectively, improving by 2.5% and 0.7% compared to YOLOv8-L. Additionally, YOLOv8-DEE outperforms other state-of-the-art methods in defect detection, demonstrating significant improvements in detecting small, medium, and large PCB defects.https://peerj.com/articles/cs-2548.pdfDefects detectionComputer visionPCBDeep learningYOLO |
| spellingShingle | Feifan Yi Ahmad Sufril Azlan Mohamed Mohd Halim Mohd Noor Fakhrozi Che Ani Zol Effendi Zolkefli YOLOv8-DEE: a high-precision model for printed circuit board defect detection PeerJ Computer Science Defects detection Computer vision PCB Deep learning YOLO |
| title | YOLOv8-DEE: a high-precision model for printed circuit board defect detection |
| title_full | YOLOv8-DEE: a high-precision model for printed circuit board defect detection |
| title_fullStr | YOLOv8-DEE: a high-precision model for printed circuit board defect detection |
| title_full_unstemmed | YOLOv8-DEE: a high-precision model for printed circuit board defect detection |
| title_short | YOLOv8-DEE: a high-precision model for printed circuit board defect detection |
| title_sort | yolov8 dee a high precision model for printed circuit board defect detection |
| topic | Defects detection Computer vision PCB Deep learning YOLO |
| url | https://peerj.com/articles/cs-2548.pdf |
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