YOLO model-based target detection algorithm for UAV images

Abstract

The increasing popularity of drones has paved the way for their utilization in various sectors, including civil, commercial, and government agencies. These unmanned aerial vehicles have proven to be invaluable in capturing images and videos from vantage points that were once difficult to access, leading to a wide range of applications. Images captured by drones often have target objects that are small in the frame and a large number of photos or videos captured, so that it is difficult for people to find the target objects in the photos. Nowadays, target detection of images captured by drones through deep learning methods, such as the YOLO algorithm, can greatly help people's work. In this paper, the authors of this paper have investigated for the last three years, for target detection of UAV images, optimization based on the original YOLO algorithm to achieve improved detection results. The research in this paper summarizes the existing research results and is of great significance to the subsequent research and application of UAV image processing.