Leather Defect Detection Based on Improved YOLOv8 Model
Addressing the low accuracy and slow detection speed experienced by algorithms based on deep learning for a leather defect detection task, a lightweight and improved leather defect detection algorithm, dubbed YOLOv8-AGE, has been proposed based on YOLOv8n. In the backbone network, the EMA attention...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| author | Zirui Peng Chen Zhang Wei Wei |
| author_facet | Zirui Peng Chen Zhang Wei Wei |
| author_sort | Zirui Peng |
| collection | DOAJ |
| description | Addressing the low accuracy and slow detection speed experienced by algorithms based on deep learning for a leather defect detection task, a lightweight and improved leather defect detection algorithm, dubbed YOLOv8-AGE, has been proposed based on YOLOv8n. In the backbone network, the EMA attention mechanism and C2f module have been fused into the C2f-EMA module, achieving performance enhancement with lower computational overhead. In the neck, the AFPN structure has been combined with the VoV-GSCSP module constructed using GSConv, to reduce the number of parameters while enhancing the model’s multi-scale detection capability. Finally, a shared convolutional layer has been introduced for simplifying the design of the detection head. Experimental results demonstrate that the improved algorithm achieves an improvement of 1.39% in mAP50 and reduces the number of parameters and GFLOPs by 9.3% and 7.41%, respectively, as compared to the original YOLOv8 model. On the dataset in this paper, there is an improvement in accuracy and detection speed over mainstream algorithms. |
| format | Article |
| id | doaj-art-87169c6cc1574c2bbdde780d2f1bbd7c |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-87169c6cc1574c2bbdde780d2f1bbd7c2025-08-20T02:53:31ZengMDPI AGApplied Sciences2076-34172024-12-0114241156610.3390/app142411566Leather Defect Detection Based on Improved YOLOv8 ModelZirui Peng0Chen Zhang1Wei Wei2School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaDepartment of Electronic Information Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, ChinaAddressing the low accuracy and slow detection speed experienced by algorithms based on deep learning for a leather defect detection task, a lightweight and improved leather defect detection algorithm, dubbed YOLOv8-AGE, has been proposed based on YOLOv8n. In the backbone network, the EMA attention mechanism and C2f module have been fused into the C2f-EMA module, achieving performance enhancement with lower computational overhead. In the neck, the AFPN structure has been combined with the VoV-GSCSP module constructed using GSConv, to reduce the number of parameters while enhancing the model’s multi-scale detection capability. Finally, a shared convolutional layer has been introduced for simplifying the design of the detection head. Experimental results demonstrate that the improved algorithm achieves an improvement of 1.39% in mAP50 and reduces the number of parameters and GFLOPs by 9.3% and 7.41%, respectively, as compared to the original YOLOv8 model. On the dataset in this paper, there is an improvement in accuracy and detection speed over mainstream algorithms.https://www.mdpi.com/2076-3417/14/24/11566attention mechanismconvolution moduledeep learningYOLOv8 |
| spellingShingle | Zirui Peng Chen Zhang Wei Wei Leather Defect Detection Based on Improved YOLOv8 Model Applied Sciences attention mechanism convolution module deep learning YOLOv8 |
| title | Leather Defect Detection Based on Improved YOLOv8 Model |
| title_full | Leather Defect Detection Based on Improved YOLOv8 Model |
| title_fullStr | Leather Defect Detection Based on Improved YOLOv8 Model |
| title_full_unstemmed | Leather Defect Detection Based on Improved YOLOv8 Model |
| title_short | Leather Defect Detection Based on Improved YOLOv8 Model |
| title_sort | leather defect detection based on improved yolov8 model |
| topic | attention mechanism convolution module deep learning YOLOv8 |
| url | https://www.mdpi.com/2076-3417/14/24/11566 |
| work_keys_str_mv | AT ziruipeng leatherdefectdetectionbasedonimprovedyolov8model AT chenzhang leatherdefectdetectionbasedonimprovedyolov8model AT weiwei leatherdefectdetectionbasedonimprovedyolov8model |