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<p>Object detection at coal transportation railway crossings is crucial for accident prevention and traffic efficiency improvement. However, the application of existing methods on resource-constrained devices has seldom been considered. To address these challenges, in this paper, we propose a lightweight railway crossing object detection algorithm based on the Transformer framework, referred to as Light-Weight DEtection TRansformer (LW-DETR). In this algorithm, the Paddle Paddle-Lightweight CPU Convolutional Network (PP-LCNet) is employed as the backbone network, where standard convolution is combined with depthwise separable convolution for multi-scale feature extraction. Furthermore, the cross-scale feature fusion module is optimized to reduce redundant calculations and enhance feature fusion efficiency. Moreover, the Scylla-Intersection over Union loss function is introduced to comprehensively evaluate bounding box similarity, thereby improving object detection accuracy. Ablation experiments conducted on a modified Pascal Visual Object Classes (Pascal VOC) dataset demonstrate that LW-DETR, while maintaining acceptable detection accuracy, achieves a 135.3% increase in frames per second, a 71.7% reduction in parameters, and a 73.7% decrease in computational load, leading to effective lightweight performance. Comparative experiments with other popular object detection algorithms further confirm that LW-DETR significantly enhances detection speed while maintaining high accuracy, considerably reducing model size and validating the effectiveness of these improvements.</p>

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LW-DETR: a lightweight transformer-based object detection algorithm for efficient railway crossing surveillance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Baoye Song,
  • Shihao Zhao,
  • Zidong Wang,
  • Jianyu Chen,
  • Weibo Liu,
  • Xiaohui Liu

摘要

Object detection at coal transportation railway crossings is crucial for accident prevention and traffic efficiency improvement. However, the application of existing methods on resource-constrained devices has seldom been considered. To address these challenges, in this paper, we propose a lightweight railway crossing object detection algorithm based on the Transformer framework, referred to as Light-Weight DEtection TRansformer (LW-DETR). In this algorithm, the Paddle Paddle-Lightweight CPU Convolutional Network (PP-LCNet) is employed as the backbone network, where standard convolution is combined with depthwise separable convolution for multi-scale feature extraction. Furthermore, the cross-scale feature fusion module is optimized to reduce redundant calculations and enhance feature fusion efficiency. Moreover, the Scylla-Intersection over Union loss function is introduced to comprehensively evaluate bounding box similarity, thereby improving object detection accuracy. Ablation experiments conducted on a modified Pascal Visual Object Classes (Pascal VOC) dataset demonstrate that LW-DETR, while maintaining acceptable detection accuracy, achieves a 135.3% increase in frames per second, a 71.7% reduction in parameters, and a 73.7% decrease in computational load, leading to effective lightweight performance. Comparative experiments with other popular object detection algorithms further confirm that LW-DETR significantly enhances detection speed while maintaining high accuracy, considerably reducing model size and validating the effectiveness of these improvements.