Object Detection of UAV Aerial Image based on YOLOv8

Abstract

With the development of technology, unmanned aerial vehicles (UAVs) have shed their military uses and gradually expanded to civilian and commercial fields. With the development of drone technology, object detection technology based on deep learning has become an important research topic in the field of drone applications. Apply object detection technology to unmanned aerial vehicles to achieve object detection and recognition of ground scenes from an aerial perspective. However, in aerial images taken by drones, the detection objects are mostly small targets, and the target scale changes greatly due to the influence of aerial perspective; The image background is complex, and the target object is easily occluded. It has brought many challenges to the target detection of unmanned aerial vehicles. Conventional object detection algorithms cannot guarantee detection accuracy when applied to drones, and optimizing the target detection performance of drones has become an important research topic in the field of drone applications. We improve the WIoUv3 loss function on the basis of YOLOv8s to reduce regression localization loss during training and improve the regression accuracy of the model. The experimental results indicate that the improved model mAP@0.5 It increased by 0.6 percentage points to 40.7%.