| Citation: | CHEN Yingxue, DING Wenrui, LI Hongguang, et al. Vehicle detection in UAV image based on video interframe motion estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(3): 634-642. doi: 10.13700/j.bh.1001-5965.2019.0279(in Chinese)
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The lightweight neural network embedded on artificial intelligence (AI) chips can realize the onboard automatic detection of vehicle objects in unmanned aerial vehicle (UAV) videos, which is important in practical applications. In this paper, a vehicle object detection algorithm in UAV videos is proposed, and then deployed and tested on AI chips. For the proposed detection algorithm, firstly, the MobileNet-SSD network is clipped based on the range of vehicle objects' size in UAV images to construct a lightweight single-frame object detector. Secondly, the interframe motion estimation was introduced to improve the poor detection performance which is usually caused by small object characteristics and lightweight network. Thirdly, the position range of missing objects in the current frame is predicted according to the information of adjacent frames. Finally, the predicted position is corrected by detection results, and the recall of lost objects is realized. Additionally, a high-quality UAV image vehicle dataset was built by fusion and automatic supplementary annotation of multiple datasets. The proposed algorithm is verified on the embedded development platform based on RK3399 chip. The results show that the network with the proposed algorithm can significantly reduce the occupation of storage resources with the lightweight characteristics. Compared to the traditional single-image detection algorithm, the proposed algorithm can effectively improve the detection accuracy. Moreover, detection speed can be as low as 125.3 ms per frame on the AI chip.
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