An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research that utilizes learning techniques adjustments to frameworks and the significance of datasets. This paper proposed the YOLOv5 model to detect the object in bad weather conditions. The paper presents the comparison between the YOLOv5 model presented in literature and the proposed YOLOv5 model in terms of accuracy, precision, recall and F1-score. The evaluation results prove the better performance of our proposed model.