Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

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.

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

“…e main process is shown in Figure 1. e structure of the YOLOv3 model [11][12][13][14][15] is shown in Figure 2. Each square in Figure 2 is a basic block consisting of Conv2d, BatchNorm2d, and LeakyReLU (except the last layer of each output) called Basic_Block.…”