An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO
Abstract 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 netwo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-85721-9 |
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| _version_ | 1832585896239562752 |
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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 |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1b7ad816b4f740d5b48a7d7b8616a608 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1b7ad816b4f740d5b48a7d7b8616a6082025-01-26T12:23:50ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-85721-9An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLOQiaoqiao Xiong0Qipeng Chen1Saihong Tang2Yiting Li3Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra MalaysiaSchool of Mechanical Engineering, Guiyang UniversityDepartment of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra MalaysiaCollege of Big Data Statistics, Guizhou University of Finance and EconomicsAbstract 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.https://doi.org/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 Scientific Reports |
| 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 | https://doi.org/10.1038/s41598-025-85721-9 |
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