YOLO Algorithms Performance Comparison for Object Detection in Adverse Weather Conditions

Abstract

This paper aimed to evaluate and compare the performance of various versions of the YOLO object detection (OD) algorithm under adverse weather conditions, including rain, fog, snow, and sand, during both day and night. A dataset of images captured in these conditions was used to carry out the performance, in terms of OD, of the previous last five versions of YOLO (You Only Look Once), using a cloud-based platform, named Google Colab Notebook. The obtained results showed that YOLOv7 outperformed the other versions in terms of both speed and accuracy. It was the fastest algorithm, completing OD in under 17.4 milliseconds, and it had the highest detection rate for the most classes in the dataset. These findings suggest that YOLOv7 is the best option for OD in adverse weather conditions and under challenging lighting conditions.