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. 2025 Aug 6;20(8):e0329303.
doi: 10.1371/journal.pone.0329303. eCollection 2025.

Automatic detection of foreign object intrusion along railway tracks based on MACENet

Xichun Chen  1 , Yu Tian  1 , Ming Li  2 , Bin Lv  1 , Shuo Zhang  3 , Zixian Qu  4 , Jianqing Wu  3 , Shiya Cheng  1
Affiliations

Automatic detection of foreign object intrusion along railway tracks based on MACENet

Xichun Chen et al. PLoS One. .

Abstract

Ensuring high accuracy and efficiency in foreign object intrusion detection along railway lines is critical for guaranteeing railway operational safety under limited resource conditions. However, current visual detection methods generally exhibit limitations in effectively handling diverse object shapes, scales, and varying environmental conditions, while typically incurring substantial computational overhead. To overcome these limitations, this study proposes a multi-level feature aggregation and context enhancement network (MACE-Net). The network architecture integrates the GOLD-YOLO module, an advanced object detection approach, alongside the updated deformable convolutional networks (DCNv3). The incorporation of DCNv3 allows the model to dynamically adapt its sampling positions according to actual object shapes, significantly enhancing feature extraction accuracy, especially for irregularly shaped intrusions. Additionally, the convolutional block attention module (CBAM) is employed to refine spatial and channel-wise feature representation, enabling the model to emphasize crucial object characteristics without substantially increasing computational complexity. Meanwhile, to improve localization robustness, the generalized intersection over union (GIoU) loss function is implemented, offering more reliable detection across various object sizes and shapes. Furthermore, to address the shortage of domain-specific datasets, we created a railway intrusion dataset comprising 7,200 images. Experimental results demonstrate that MACE-Net achieves superior detection performance, improving mAP@0.5 from 78.9% (baseline YOLOv8) to 83.8%-a notable increase of 4.9%. Meanwhile, the F1-score also rises by 5.2%. Importantly, despite significant accuracy gains, MACE-Net maintains computational efficiency similar to that of the baseline, affirming its suitability for real-time railway foreign object detection tasks under constrained energy and computational environments.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig 1. Multi-level feature aggregation and context enhancement network.
Fig 2
Fig 2. Architecture of GOLD-YOLO.
Fig 3
Fig 3. Low-stage gather-and-distribute branch.
Fig 4
Fig 4. High-stage gather-and-distribute branch.
Fig 5
Fig 5. Standard convolution kernel and deformable convolution kernel.
Fig 6
Fig 6. Schematic diagram of the structure perception module.
(a) illustration of DCNv3; (b) illustration of C2f_DCNv3.
Fig 7
Fig 7. Architecture of CBAM.
Fig 8
Fig 8. Diagram of IoU ratio.
Fig 9
Fig 9. Illustration of the dataset.
Fig 10
Fig 10. Performance comparison of mainstream detection models for railway intrusion detection.
Fig 11
Fig 11. Visualized railway intrusion detection results of comparative models.
Fig 12
Fig 12. Examples of false-positive and false-negative detections.
See this image and copyright information in PMC

References

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