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Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions

Author

Listed:
  • Dan Zhong

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Tiehu Li

    (School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

  • Zhang Pan

    (The Air Traffic Control Bureau of Civil Aviation Administration of China, Beijing 100022, China)

  • Jinxiang Guo

    (The Northwest Air Traffic Control Bureau of Civil Aviation Administration of China, Xi’an 710000, China)

Abstract

Taking all-day, all-weather airport security protection as the application demand, and aiming at the lack of complex meteorological conditions processing capability of current remote sensing image aircraft target detection algorithms, this paper takes the YOLOX algorithm as the basis, reduces model parameters by using depth separable convolution, improves feature extraction speed and detection efficiency, and at the same time, introduces different cavity convolution in its backbone network to increase the perceptual field and improve the model’s detection accuracy. Compared with the mainstream target detection algorithms, the proposed YOLOX-DD algorithm has the highest detection accuracy under complex meteorological conditions such as nighttime and dust, and can efficiently and reliably detect the aircraft in other complex meteorological conditions including fog, rain, and snow, with good anti-interference performance.

Suggested Citation

  • Dan Zhong & Tiehu Li & Zhang Pan & Jinxiang Guo, 2023. "Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions," Sustainability, MDPI, vol. 15(14), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11463-:d:1201465
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