Improved RT-DETR Framework for Railway Obstacle Detection
Obstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. Building upon the RT-DETR f...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11080059/ |
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| author | Peng Li Yanhui Peng Su-Mei Wang Cheng Zhong |
| author_facet | Peng Li Yanhui Peng Su-Mei Wang Cheng Zhong |
| author_sort | Peng Li |
| collection | DOAJ |
| description | Obstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. Additionally, a Deformable Agent Attention (DAA) mechanism is designed to optimize attention weights through sparse queries, effectively improving detection accuracy for small targets and enhancing inference speed in complex scenarios. Experimental results demonstrate that the improved model achieves 87.9% mean average precision (mAP) on a railway dataset, with a detection speed of 90 frames per second (FPS). The proposed model achieves a +1.7% mAP improvement and 13.9% faster inference speed compared to RT-DETR, while simultaneously reducing model parameters by 24.6%. As a result, the proposed model is highly effective for multiple obstacle intrusion detection in complex real-world scenarios. |
| format | Article |
| id | doaj-art-a30fdd0b100a43588bed62797ea1bb2e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a30fdd0b100a43588bed62797ea1bb2e2025-08-20T03:31:47ZengIEEEIEEE Access2169-35362025-01-011312586912588010.1109/ACCESS.2025.358915911080059Improved RT-DETR Framework for Railway Obstacle DetectionPeng Li0https://orcid.org/0000-0001-9558-5088Yanhui Peng1Su-Mei Wang2https://orcid.org/0000-0002-1285-1553Cheng Zhong3School of Railway Tracks and Transportation, Wuyi University, Jiangmen, ChinaSchool of Railway Tracks and Transportation, Wuyi University, Jiangmen, ChinaNational Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), Guangzhou, Hong KongSchool of Railway Tracks and Transportation, Wuyi University, Jiangmen, ChinaObstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. Additionally, a Deformable Agent Attention (DAA) mechanism is designed to optimize attention weights through sparse queries, effectively improving detection accuracy for small targets and enhancing inference speed in complex scenarios. Experimental results demonstrate that the improved model achieves 87.9% mean average precision (mAP) on a railway dataset, with a detection speed of 90 frames per second (FPS). The proposed model achieves a +1.7% mAP improvement and 13.9% faster inference speed compared to RT-DETR, while simultaneously reducing model parameters by 24.6%. As a result, the proposed model is highly effective for multiple obstacle intrusion detection in complex real-world scenarios.https://ieeexplore.ieee.org/document/11080059/Convolutional neural network (CNN)deep learningobstacle intrusion detectionrailway traffictransformer |
| spellingShingle | Peng Li Yanhui Peng Su-Mei Wang Cheng Zhong Improved RT-DETR Framework for Railway Obstacle Detection IEEE Access Convolutional neural network (CNN) deep learning obstacle intrusion detection railway traffic transformer |
| title | Improved RT-DETR Framework for Railway Obstacle Detection |
| title_full | Improved RT-DETR Framework for Railway Obstacle Detection |
| title_fullStr | Improved RT-DETR Framework for Railway Obstacle Detection |
| title_full_unstemmed | Improved RT-DETR Framework for Railway Obstacle Detection |
| title_short | Improved RT-DETR Framework for Railway Obstacle Detection |
| title_sort | improved rt detr framework for railway obstacle detection |
| topic | Convolutional neural network (CNN) deep learning obstacle intrusion detection railway traffic transformer |
| url | https://ieeexplore.ieee.org/document/11080059/ |
| work_keys_str_mv | AT pengli improvedrtdetrframeworkforrailwayobstacledetection AT yanhuipeng improvedrtdetrframeworkforrailwayobstacledetection AT sumeiwang improvedrtdetrframeworkforrailwayobstacledetection AT chengzhong improvedrtdetrframeworkforrailwayobstacledetection |