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<p>The detection of foreign objects in key components of high-speed trains is critical for railway safety, but existing methods struggle under low-light conditions, complex backgrounds, and small objects. To address these issues, we propose an efficient detection framework integrating an adaptive brightness enhancement network (ABEN) and a lightweight train foreign object detection network (LTFD-Net). ABEN adaptively enhances images according to their illumination, improving clarity across multiple objects and backgrounds while ensuring real-time processing. LTFD-Net combines a lightweight backbone with a multi-dimensional feature enhancement module, capturing multi-scale and contextual features to accurately detect small and complex defects with minimal computational overhead. To support realistic evaluation, we introduce the high-speed train foreign object detection (HTFD) dataset with 3,904 annotated images across five key components. Experiments show that the integrated framework achieves 85.4% mean average precision (mAP) and 112 frames per second (FPS) on HTFD, surpassing state-of-the-art methods. Independently, LTFD-Net reaches 78.3% mAP and 97 FPS on NEU-DET, demonstrating preliminary generalization capability without illumination enhancement. This work provides an efficient and practical solution for real-time foreign object detection under challenging railway conditions.</p>

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A lightweight foreign object detection framework for high-speed trains with adaptive illumination enhancement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Hui Lin,
  • Junqi Li,
  • Baolin Liu,
  • Xueshan Li

摘要

The detection of foreign objects in key components of high-speed trains is critical for railway safety, but existing methods struggle under low-light conditions, complex backgrounds, and small objects. To address these issues, we propose an efficient detection framework integrating an adaptive brightness enhancement network (ABEN) and a lightweight train foreign object detection network (LTFD-Net). ABEN adaptively enhances images according to their illumination, improving clarity across multiple objects and backgrounds while ensuring real-time processing. LTFD-Net combines a lightweight backbone with a multi-dimensional feature enhancement module, capturing multi-scale and contextual features to accurately detect small and complex defects with minimal computational overhead. To support realistic evaluation, we introduce the high-speed train foreign object detection (HTFD) dataset with 3,904 annotated images across five key components. Experiments show that the integrated framework achieves 85.4% mean average precision (mAP) and 112 frames per second (FPS) on HTFD, surpassing state-of-the-art methods. Independently, LTFD-Net reaches 78.3% mAP and 97 FPS on NEU-DET, demonstrating preliminary generalization capability without illumination enhancement. This work provides an efficient and practical solution for real-time foreign object detection under challenging railway conditions.