Citation: | ZHANG Ruixin, LI Ning, ZHANG Xiaxia, et al. Low-altitude UAV detection method based on optimized CenterNet[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2335-2344. doi: 10.13700/j.bh.1001-5965.2021.0108(in Chinese) |
To achieve effective detection of "low, slow and small" unmanned aerial vehicle(UAV)and improve detection accuracy and positioning quality, we propose a low-altitude UAV detection method based on joint attention and CenterNet. Aiming at the problem of high miss-detection rate of small targets in general target detection algorithms, a decoupled non-local operator is introduced to capture the relevance of target regions in optical images. Utilizing the similarity between individuals of the UAV group, the discrete UAV features are correlated to each other to reduce the missed detection rate. Moreover, to obtain more accurate detection boxes, we optimized the label coding strategy and bounding box regression method of CenterNet, and the positioning quality loss is introduced to improve the positioning quality of the detection boxes. Experimental results show that the optimized S-CenterNet algorithm has an average accuracy increase of 8.9% compared with the original CenterNet, and the detection boxer positioning quality has been significantly improved.
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