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A BiFPN-SECA detection network for foreign objects on top of railway freight vehicles

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Abstract

Foreign object detection on top of railway freight vehicles is critical to ensuring the safety and efficiency railway transportation. However, this task is mainly carried out manually, facing major challenges such as low accuracy and false positives and false negatives. To address the above problems, this paper proposes a novel BiFPN-SECA Network, Bidirectional Feature Pyramid Network with Squeeze-and-Excitation Channel Attention, which integrates an attention mechanism combining contextual features and coordinate information. This innovative approach improves detection performance by effectively capturing and emphasizing key features. The ordinary convolutional layers in the coordinate attention mechanism are improved in SEnet to increase the attention to the regions of interest. Second, an improved coordinate attention mechanism module is embedded before the BiFPN sampling layer and after the backbone output layer to enhance key feature extraction. In addition, a dataset of foreign objects in railway freight vehicles was established, including bag, stone and torn. Experimental results verify that the BiFPN-SECA Network is able to improve detection accuracy, with an accuracy of 94.57\(\%\), demonstrating its potential to significantly enhance the safety and reliability of railway freight.

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Availability of data and materials

The information related to the railway freight scenario is confidential information, and there is no public data set in practice, so the data set used in this article cannot be used publicly.

Code Availability

The code is publicly available.

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Acknowledgements

This work is part of the National Key Research and Development Program 2022YFB2602205, in part by Science and Technology Program in Xi’an city under Grant 21XJZZ0055, in part by Natural Science Foundation of Shaanxi Provincial Department of Education under Grant 22JK0474.

Funding

Science and Technology Program in Xi’an city under Grant 21XJZZ0055, Natural Science Foundation of Shaanxi Provincial Department of Education under Grant 22JK0474.

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Author notes

  1. Yiqing Yang, Ting Cao and Yi Zhu have contributed equally to this work.

Authors and Affiliations

  1. Xi’an University of Technology, Street, Xi’an, 710048, Shaanxi, China

    Sheng Liu, Yiqing Yang & Ting Cao

  2. China Railway First Survey and Design Institute Group Co., Ltd, Xi’an, 710043, Shaanxi, China

    Yi Zhu

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  1. Sheng Liu
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  2. Yiqing Yang
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  3. Ting Cao
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Contributions

Sheng Liu & Ting Cao: Preparation of manuscripts and presentation of methodology. Yiqing Yang & Yi Zhu: Data collection, charting, and coding.

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Correspondence to Ting Cao.

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Liu, S., Yang, Y., Cao, T. et al. A BiFPN-SECA detection network for foreign objects on top of railway freight vehicles. SIViP 18, 9027–9035 (2024). https://doi.org/10.1007/s11760-024-03527-0

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  • DOI: https://doi.org/10.1007/s11760-024-03527-0

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