Abstract
Most existing methods are difficult to detect low-altitude and fast-moving drones. A low-altitude unmanned aerial vehicle (UAV) target detection method based on an improved YOLOv3 network is proposed. While keeping the basic framework of the original model unchanged, the YOLOv3 model is improved. That is, multiscale prediction is added to enhance the detection ability of small-target objects. In addition, the two-axis Pan/Tilt/Zoom (PTZ) camera is controlled based on proportional integral derivative (PID), so that the target tends to the center of the field of view. It is more conducive to accurate detection. Finally, experiments are carried out using real UAV datasets. The results show that the mean average precision (mAP), AP50, and AP75 are 25.12%, 39.75%, and 26.03%, respectively, which are better than other methods. Also, the frame rate is 21 frames·s−1, which meets the performance requirements.
Highlights
With the rise and development of unmanned aerial vehicle (UAV) technology, it has been widely used in military and civil fields
A large number of UAVs pose a certain threat to the flight safety of aircraft and the political sensitivity of images in confidential areas
For the sake of public safety, local governments prohibit unauthorized UAV flight in airports, meeting places, and other areas [2]. erefore, monitoring UAVs in specific areas is an urgent need for security
Summary
With the rise and development of UAV technology, it has been widely used in military and civil fields. Li et al [9] proposed a fast and effective moving target detection method, which extracts cross features based on line segments. It has good detection speed and rotation accuracy, but the application scope of the algorithm is small and the practical application has great limitations. Xiaofei [13] proposes a UAV multitarget tracking and path planning method combining basic gray wolf optimizer and Gaussian distribution estimation It overcomes the problem of real-time optimization of complex projects with traditional models and has good effectiveness and practicability. Based on the above analysis, a target detection algorithm based on the improved YOLOv3 network is proposed for the problem of low-altitude UAV target detection.
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