Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions

Dan Zhong (), Tiehu Li, Zhang Pan and Jinxiang Guo
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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

Sustainability, 2023, vol. 15, issue 14, 1-12

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.

Keywords: remote sensing images; aircraft target detection; complex meteorological conditions; YOLOX algorithm; depth separable convolution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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