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ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance

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Abstract

The airport surface surveillance system is essential in ensuring airport safety and maximizing the efficient utilization of airport resources. Current airport detection algorithms suffer from few and lack relevant airport target data. To solve these issues, this paper establishes two airport datasets named ASS-Dataset, including the surveillance dataset and the panoramic surveillance dataset. Compared to other aircraft datasets, our datasets are collected from authentic airport surface surveillance systems. According to observation, most objects are small in datasets. This paper proposes a small object detection method ASSD-YOLO based on improved YOLOv7. First, the designed f-efficient attention module is added to the backbone network to improve the accuracy of the algorithm. Second, the transformer encoder network is incorporated into the backbone network to increase feature extraction. Finally, the small target detection layer is added to the head network to improve the ability to extract small targets. The model of the mean average precision is 93.5\(\%\) in the surveillance dataset. In the panoramic surveillance dataset, the ASSD-YOLO achieves 10.8\(\%\) and 21.4\(\%\) higher average precision for the airplane and truck than YOLOv7. g. Comparing the method proposed in this paper to the original YOLOv7, the performance improvement for the mAP is 4.6\(\%\) when using the RSOD open dataset. ASS-Dataset is available at https://github.com/rookie257/rookie257.github.io. Code is available at https://github.com/rookie257/small_detection.github.io.

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Data Availability

Data is available at https://github.com/rookie257/rookie257.github.io.

Code Availability

Code is available at https://github.com/rookie257/small_detection.github.io.

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Acknowledgements

This work is supported by the National Key R &D Program of China(No.2021YFF0603904) and the Key Projects of Heilongjiang Provincial Natural Science Foundation(No.ZD2022F001).

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Author notes

  1. Chengtao Cai and Liying Zheng contributed equally to this work.

Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Wentao Zhou, Chengtao Cai, Chenming Li & Daohui Zeng

  2. Heilongjiang Provincial Key Laboratory of Environment Intelligent Perception, Harbin Engineering University, Harbin, 150001, China

    Chengtao Cai

  3. Key laboratory of Intelligent Technology and Application of Marine Equipment (Harbin Engineering University), Harbin Engineering University, Harbin, 150001, China

    Chengtao Cai

  4. College of Computer Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Liying Zheng

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  1. Wentao Zhou
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  4. Chenming Li
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Contributions

Conceptualization, Wentao Zhou, Chengtao Cai and Liying Zheng; data curation, Liying Zheng; formal analysis, Wentao Zhou and Chengtao Cai; investigation, Wentao Zhou and Liying Zheng; methodology, Wentao Zhou and Chengtao Cai; project administration, Liying Zheng and Chenming Li; writings original draft, Wentao Zhou and Daohui Zeng; writing, reviewing and editing, Chenming Li. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Chengtao Cai or Liying Zheng.

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Appendix A: Section title of first appendix

Appendix A: Section title of first appendix

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Zhou, W., Cai, C., Zheng, L. et al. ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance. Multimed Tools Appl 83, 55527–55548 (2024). https://doi.org/10.1007/s11042-023-17628-4

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