Research Towards Yolo-Series Algorithms: Comparison and Analysis of Object Detection Models for Real-Time UAV Applications

Abstract

General Unmanned Aerial Vehicles (UAVs) are widely used through the computer vision functions of onboard cameras and embedded systems. However, due to the limited memory and computing power of embedded devices on the UAV platform, it is a very challenging issue to analyze the real-time scene through the object detection method. To deal with these challenges, this paper compares the performance of different Yolo series models on the Pascal VOC dataset, using mAP and FPS as evaluation metrics, and applies the training results to the XTDrone UAV Simulation Platform for testing. We evaluate YOLOv3, YOLOv3-tiny, YOLOv3-SPP3, YOLOv4, and YOLOv4-tiny on the Pascal VOC benchmark dataset; the mAP of YOLOv4 is 87.48%, which is 14.2% higher than that of YOLOv3. FPS reaches 72, and the test time on the test set is 103.86s; the shortest test time on the validation set is Yolov4-tiny, but the mAP only reaches 50.06%, which is not as good as Yolov3-tiny. This paper compares the performance of five models in the Pascal VOC Dataset and simulates them on the XTDrone platform, and finally concludes that Yolov3-Tiny can meet the requirements of real-time, lightweight, and high precision.