An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO
Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Nature Research
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120220/ http://psasir.upm.edu.my/id/eprint/120220/1/120220.pdf http://psasir.upm.edu.my/id/eprint/120220/2/120220.pdf |
| _version_ | 1848868140410208256 |
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| author | Qiaoqiao, Xiong Qipeng, Chen Saihong, Tang Yiting, Li |
| author_facet | Qiaoqiao, Xiong Qipeng, Chen Saihong, Tang Yiting, Li |
| author_sort | Qiaoqiao, Xiong |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering. Secondly, improvements were made to the model’s feature extraction quality by encoding inter-channel information using efficient multi-Scale attention to consider channel importance. Precise integration of spatial structural and channel information further enhanced the model’s overall feature extraction capability. Finally, EIoU Loss replaced CIoU Loss to address bounding box aspect ratio variability and sample imbalance challenges, significantly improving overall detection task performance. The algorithm’s performance was evaluated using a dataset to detect stranded elastic needle defects. The experimental results indicate that the enhanced VEE-YOLO model’s size decreased from 6.096 M to 5.486 M, while the detection speed increased from 179FPS to 244FPS, achieving a mAP of 0.926. Remarkable advancements across multiple metrics make it well-suited for deploying deep detection models in complex industrial environments. |
| first_indexed | 2025-11-15T14:47:39Z |
| format | Article |
| id | upm-120220 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T14:47:39Z |
| publishDate | 2025 |
| publisher | Nature Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1202202025-09-29T06:33:41Z http://psasir.upm.edu.my/id/eprint/120220/ An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO Qiaoqiao, Xiong Qipeng, Chen Saihong, Tang Yiting, Li Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering. Secondly, improvements were made to the model’s feature extraction quality by encoding inter-channel information using efficient multi-Scale attention to consider channel importance. Precise integration of spatial structural and channel information further enhanced the model’s overall feature extraction capability. Finally, EIoU Loss replaced CIoU Loss to address bounding box aspect ratio variability and sample imbalance challenges, significantly improving overall detection task performance. The algorithm’s performance was evaluated using a dataset to detect stranded elastic needle defects. The experimental results indicate that the enhanced VEE-YOLO model’s size decreased from 6.096 M to 5.486 M, while the detection speed increased from 179FPS to 244FPS, achieving a mAP of 0.926. Remarkable advancements across multiple metrics make it well-suited for deploying deep detection models in complex industrial environments. Nature Research 2025 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/120220/1/120220.pdf text en cc_by_4 http://psasir.upm.edu.my/id/eprint/120220/2/120220.pdf Qiaoqiao, Xiong and Qipeng, Chen and Saihong, Tang and Yiting, Li (2025) An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO. Scientific Reports, 15 (1). art. no. 2879. pp. 1-19. ISSN 2045-2322 https://www.nature.com/articles/s41598-025-85721-9?error=cookies_not_supported&code=1cd19737-28e4-478f-a6de-a9518c54d657 10.1038/s41598-025-85721-9 |
| spellingShingle | Qiaoqiao, Xiong Qipeng, Chen Saihong, Tang Yiting, Li An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title | An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title_full | An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title_fullStr | An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title_full_unstemmed | An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title_short | An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO |
| title_sort | efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using vee-yolo |
| url | http://psasir.upm.edu.my/id/eprint/120220/ http://psasir.upm.edu.my/id/eprint/120220/ http://psasir.upm.edu.my/id/eprint/120220/ http://psasir.upm.edu.my/id/eprint/120220/1/120220.pdf http://psasir.upm.edu.my/id/eprint/120220/2/120220.pdf |