Abstract
Vehicle target detection technology refers to the process of vehicle detection and recognition in the image data set of different backgrounds by means of feature extraction. The vehicle target detection technology based on deep learning shows obvious advantages in the accuracy and speed of target detection. With the development of science and technology, the detection and recognition of vehicles in UAV aerial images has become an important applied research direction. This paper studies the detection and recognition of UAV aerial vehicle based on deep learning, and proposes a new deep learning-based algorithm to solve the problem that incomplete vehicle targets in the UAV aerial vehicle based on YOLOV3 algorithm cannot be recognized, and vehicles close to the UAV aerial vehicle are missed. Experimental verification results show that, compared with the existing algorithms, the proposed algorithm can significantly improve the detection accuracy of UAV aerial vehicle based on deep learning while ensuring real-time performance.
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