Object Detection of Autonomous Vehicles under Adverse Weather Conditions

The computer vision systems that are responsible for driving Autonomous Vehicles (AV) are evaluated based on their capacity to recognize obstacles and objects located close to the vehicle in a variety of settings. An essential obstacle when it is related to computer vision is finding a way to improve the capacity of an AV to differentiate between the components of its surroundings, even when operating in challenging conditions. For instance, unfavorable weather conditions such as fog and rain can cause image corruption, which in turn can result in a significant reduction in the performance of Object Detection (OD). The primary navigation of AVs is dependent on the efficacy of the image processing algorithms that are used for the data collected from the many different visual sensors. This information is gathered by the vehicle itself. Therefore, it is of the utmost importance to cultivate the capacity to recognize items such as road lanes, automobiles, and pedestrians under adverse conditions such as bad weather. The main purpose of this article is to examine the related works concerning weather detection and OD. Significant objects such as road lanes, vehicles, and pedestrians are considered for review.