An enhanced YOLOv5 based on Color Harmony Algorithm for object detection in Unmanned Aerial Vehicle captured Images

Abstract

Abstract Recent improvements in robotics and computer vision enable new camera-equipped drone applications. Aerial Object Detection (OD) is one. Despite recent advances, computer vision OD remains difficult. Due to UAVs' fast speed, different views, and fluctuating altitudes, objects in Unmanned Aerial Vehicle (UAV) photos are heterogeneous, fluctuate in size, and are dense, making OD challenging with existing algorithms. Existing object recognition algorithms perform worse on UAV images because OD in aerial images is more difficult than in ground-taken images. If a generic object detection technique is used to drone-captured images, its performance will be drastically degraded owing to the fact that varied surroundings with complicated background and the size of objects are at the core of this phenomena. In this work, an object detection model CHA-YOLOv5 is proposed. In the proposed system the detection module is optimized using the Color Harmony Algorithm, as it determines the prediction head from the available three prediction heads. CHA-YOLOv5 accurately predicts several bounding boxes per grid cell. The proposed model has been trained using several images from challenging contexts. The experimental results show that the objects are identified accurately using the proposed improved network model in this study. It achieved precision 98.675%, pecall 97.8023%, mAP 97.23%, F1 score 98.50%. Our system outperformed the YOLOV5, ResNet50, VGG19, and InceptionNet V3.