LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard
Current algorithms for surface defect detection in particleboard often encounter limitations such as high computational complexity and excessive parameter scale. To address these challenges, this study proposes the LE-YOLO model, which incorporates a normalized Wasserstein distance into the loss fun...
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| Format: | Article |
| Language: | English |
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North Carolina State University
2025-07-01
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| Series: | BioResources |
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| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24732 |
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| _version_ | 1849240527860400128 |
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| author | Chao He Yongkang Kang Anning Ding Wei Jia Huaqiong Duo |
| author_facet | Chao He Yongkang Kang Anning Ding Wei Jia Huaqiong Duo |
| author_sort | Chao He |
| collection | DOAJ |
| description | Current algorithms for surface defect detection in particleboard often encounter limitations such as high computational complexity and excessive parameter scale. To address these challenges, this study proposes the LE-YOLO model, which incorporates a normalized Wasserstein distance into the loss function to enhance the detection capability for minute surface defects. A dynamic mixed convolutional network module is introduced to construct a lightweight backbone architecture. Moreover, the Shared Dilated Feature Pyramid (SDFP) module is employed in the neck network, effectively reducing computational overhead while preserving detection accuracy. A lightweight detection head was further designed, integrating shared convolutional operations with a distribution-aware loss function, thereby substantially improving detection performance in complex textured environments. Experimental evaluations conducted on the Chipboardv1.0 particleboard surface defect dataset demonstrated that compared to the baseline YOLOv11n model, LE-YOLO achieved a 5% improvement in recall, a 1% increase in F1 score, a 4% enhancement in mAP@50, a 6% gain in mAP@50–95, a 12.69% acceleration in inference speed, and an 18.6% reduction in parameter count. Compared with other models, the proposed approach not only improved detection precision but also effectively reduced model complexity, achieving a lightweight and efficient detection framework. |
| format | Article |
| id | doaj-art-956909d0abdb462c86c9c0831bc0d807 |
| institution | Kabale University |
| issn | 1930-2126 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj-art-956909d0abdb462c86c9c0831bc0d8072025-08-20T04:00:33ZengNorth Carolina State UniversityBioResources1930-21262025-07-01203717971933055LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on ParticleboardChao He0Yongkang Kang1Anning Ding2Wei Jia3Huaqiong Duo4College of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia Autonomous Region, ChinaCollege of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia Autonomous Region, ChinaCollege of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia Autonomous Region, ChinaCollege of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia Autonomous Region, ChinaCollege of Materials Science and Art Design, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia Autonomous Region, ChinaCurrent algorithms for surface defect detection in particleboard often encounter limitations such as high computational complexity and excessive parameter scale. To address these challenges, this study proposes the LE-YOLO model, which incorporates a normalized Wasserstein distance into the loss function to enhance the detection capability for minute surface defects. A dynamic mixed convolutional network module is introduced to construct a lightweight backbone architecture. Moreover, the Shared Dilated Feature Pyramid (SDFP) module is employed in the neck network, effectively reducing computational overhead while preserving detection accuracy. A lightweight detection head was further designed, integrating shared convolutional operations with a distribution-aware loss function, thereby substantially improving detection performance in complex textured environments. Experimental evaluations conducted on the Chipboardv1.0 particleboard surface defect dataset demonstrated that compared to the baseline YOLOv11n model, LE-YOLO achieved a 5% improvement in recall, a 1% increase in F1 score, a 4% enhancement in mAP@50, a 6% gain in mAP@50–95, a 12.69% acceleration in inference speed, and an 18.6% reduction in parameter count. Compared with other models, the proposed approach not only improved detection precision but also effectively reduced model complexity, achieving a lightweight and efficient detection framework.https://ojs.bioresources.com/index.php/BRJ/article/view/24732object detectionlightweight architectureyoloparticleboard surface defectsdeep learningfeature fusion |
| spellingShingle | Chao He Yongkang Kang Anning Ding Wei Jia Huaqiong Duo LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard BioResources object detection lightweight architecture yolo particleboard surface defects deep learning feature fusion |
| title | LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard |
| title_full | LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard |
| title_fullStr | LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard |
| title_full_unstemmed | LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard |
| title_short | LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard |
| title_sort | le yolo a lightweight and enhanced algorithm for detecting surface defects on particleboard |
| topic | object detection lightweight architecture yolo particleboard surface defects deep learning feature fusion |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24732 |
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