Fast detection and recognition method of UAV in sky background

With the development of science and technology, unmanned aerial vehicle (UAV) is more and more widely used to bring a lot of convenience to the society, but also led to serious threats to public security, personal privacy, military security and other aspects. Therefore, it is increasingly important to find unknown drones quickly and accurately. In UAV detection, the technologies based on acoustic, radio and radar detection are common, but these technologies usually require expensive equipment and strict configuration. However, the method based on machine vision has the advantages of low cost and simple configuration. In addition, detection and recognition methods based on deep learning have been fully developed, but most of them are for a single visible image, and the detection and recognition effect is limited. In this paper, a fast detection and identification method based is proposed based on the backbone of YOLOv3 (You Only Look Once version3). And dual-channel detectors were used as data sources. In this method, infrared and visible images are simultaneously input into the network for feature extraction, and the extracted depth features are concatenated. Then the multi-scale prediction network is used to regression the target location to obtain the final detection and recognition results. Finally, by collecting real UAV data sets, the network is trained and tested for comparative experiments. Experimental results show that the mAP of method in this paper is worthy of improvement, and the detection speed remains at 27images/s.