AI based Automatic Vehicle Detection from Unmanned Aerial Vehicles (UAV) using YOLOv5 Model

Abstract

Unmanned aerial vehicle (UAV) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. The challenge arises in keeping minimal computational complexity allowing the system to be real-time as well. Applications of vehicle detection from UAVs include traffic parameter estimation, violation detection, number plate reading, and parking lot monitoring. The one stage detection model, YOLOv5 is used in this research work to develop a deep neural model-based vehicle detection system on highways from UAVs. In our system, several improvised strategies are put forth that are appropriate for small vehicle recognition under an aerial view angle which can accomplish real-time detection and high accuracy by incorporating an optimal pooling approach and dense topology method. Tilting the orientation of aerial photographs can improve the system's effectiveness. Metrics like hit rate, accuracy, and precision values are used to assess the performance of the proposed hybrid model, and performance is compared to that of other state-of-the-art algorithms.