Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n
To address challenges in the detection of air duct clamps due to factors such as dim environments and small objects in train detection, such as insufficient detection accuracy and low detection efficiency, this paper proposes a RSA-YOLOv10n-based detection algorithm, developed utilizing a dataset of...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2025-04-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.02.100 |
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| _version_ | 1849224626595430400 |
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| author | WANG Dairong ZHAO Yuhong QU Xiaozhang LIU Yi |
| author_facet | WANG Dairong ZHAO Yuhong QU Xiaozhang LIU Yi |
| author_sort | WANG Dairong |
| collection | DOAJ |
| description | To address challenges in the detection of air duct clamps due to factors such as dim environments and small objects in train detection, such as insufficient detection accuracy and low detection efficiency, this paper proposes a RSA-YOLOv10n-based detection algorithm, developed utilizing a dataset of air duct clamps collected from trains. Firstly, in order to better capture diverse features in images, the Conv convolution is modified to RepConv convolution based on the YOLOv10n model, facilitating adaptive adjustments in the representation ability of the convolution kernel. Secondly, in order to improve the sensitivity of the model to important information, the StokenAttention attention mechanism is introduced at the Neck layer, supporting dynamic focus on key areas during image detection. Experiments conducted using a dataset comprising thousands of self-labeled images of air duct clamps demonstrated that the <italic>mAP</italic>@50%, <italic>mAP</italic>@95%, accuracy, and recall rate of the proposed RSA-YOLOv10n model were 98.7%, 75.1%, 97.3%, and 96.6%, respectively. These results show improvements of 0.2, 0.8, 0.2, and 0.3 percentage points compared to the YOLOv10n model. Furthermore, the RSA-YOLOv10n model proves a great improvement in the FPS value, while maintaining model size, GFLOPs, function loss value, and other parameters equivalent to those of the YOLOv10n model. Through ablation experiments and comparative experiments with other models, including YOLOv3-tiny, YOLOv5n, and YOLOv8n, the analysis and verification showcased the superiority of the proposed detection algorithm in terms of overall accuracy, recall rate and detection time, suggesting its potential for widespread application in the field of detecting air duct clamps on trains. The study findings are of great significance for the technical development of train device and component detection. |
| format | Article |
| id | doaj-art-231015d76548425cb0e20c2c74edca7e |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-231015d76548425cb0e20c2c74edca7e2025-08-25T06:57:32ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272025-04-01546488832733Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10nWANG DairongZHAO YuhongQU XiaozhangLIU YiTo address challenges in the detection of air duct clamps due to factors such as dim environments and small objects in train detection, such as insufficient detection accuracy and low detection efficiency, this paper proposes a RSA-YOLOv10n-based detection algorithm, developed utilizing a dataset of air duct clamps collected from trains. Firstly, in order to better capture diverse features in images, the Conv convolution is modified to RepConv convolution based on the YOLOv10n model, facilitating adaptive adjustments in the representation ability of the convolution kernel. Secondly, in order to improve the sensitivity of the model to important information, the StokenAttention attention mechanism is introduced at the Neck layer, supporting dynamic focus on key areas during image detection. Experiments conducted using a dataset comprising thousands of self-labeled images of air duct clamps demonstrated that the <italic>mAP</italic>@50%, <italic>mAP</italic>@95%, accuracy, and recall rate of the proposed RSA-YOLOv10n model were 98.7%, 75.1%, 97.3%, and 96.6%, respectively. These results show improvements of 0.2, 0.8, 0.2, and 0.3 percentage points compared to the YOLOv10n model. Furthermore, the RSA-YOLOv10n model proves a great improvement in the FPS value, while maintaining model size, GFLOPs, function loss value, and other parameters equivalent to those of the YOLOv10n model. Through ablation experiments and comparative experiments with other models, including YOLOv3-tiny, YOLOv5n, and YOLOv8n, the analysis and verification showcased the superiority of the proposed detection algorithm in terms of overall accuracy, recall rate and detection time, suggesting its potential for widespread application in the field of detecting air duct clamps on trains. The study findings are of great significance for the technical development of train device and component detection.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.02.100object detectionYOLOv10nair duct clampRepConv convolutionStokenAttention attention mechanism |
| spellingShingle | WANG Dairong ZHAO Yuhong QU Xiaozhang LIU Yi Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n Kongzhi Yu Xinxi Jishu object detection YOLOv10n air duct clamp RepConv convolution StokenAttention attention mechanism |
| title | Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n |
| title_full | Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n |
| title_fullStr | Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n |
| title_full_unstemmed | Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n |
| title_short | Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n |
| title_sort | detection algorithm for air duct clamp on trains based on rsa yolov10n |
| topic | object detection YOLOv10n air duct clamp RepConv convolution StokenAttention attention mechanism |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.02.100 |
| work_keys_str_mv | AT wangdairong detectionalgorithmforairductclampontrainsbasedonrsayolov10n AT zhaoyuhong detectionalgorithmforairductclampontrainsbasedonrsayolov10n AT quxiaozhang detectionalgorithmforairductclampontrainsbasedonrsayolov10n AT liuyi detectionalgorithmforairductclampontrainsbasedonrsayolov10n |