Autonomous Vehicle Detection and Tracking Based on Improved Yolov5 and Gmmpf in Harsh Weather Conditions

Abstract

Abstract Autonomous vehicles (AVs) rely on various sensory data to accurately understand their surroundings and guarantee a safe voyage. In AVs, and intelligent transportation systems, vehicle detection and tracking (VDT) are crucial. A camera's ability to perform is dangerously restricted by adverse or challenging weather conditions (CWC) like fog, rain, snow, sandstorms or dust, which all compromise driving safety by lowering visibility. These limitations affect how well the identification and tracking models used in traffic surveillance systems as well as applications for AVs function. This paper proposes autonomous VDT system using Improved You Look Only Once Version 5 (IYOLOV5) and Particle Filter based on a Gaussian Mixture Model (GMMPF) in harsh weather conditions. This paper consists of four steps: image collection, image deweathering, vehicle detection, and vehicle tracking (VT). First, the multiple roadside vehicles are collected from the datasets. Next, image deweathering is performed based on the Adaptive Automatic White balance (AAWB) method, which improves the quality of the images and preserves the edge details. Next, the IYOLOV5 algorithm is used to detect the vehicle, and finally, the vehicles are tracked using the GMMPF concept. The suggested method is evaluated and contrasted with the current methods on the DAWN and COCO datasets. The outcomes have confirmed the usefulness of the suggested solution, which outperforms cutting-edge vehicle recognition and tracking techniques in inclement weather.