Published Paper Details:

Fundamental building blocks in the field of autonomous vehicle perception, which has seen significant progress in recent years, are object detection and classification. These innovations are essential for giving cars the ability to sense and comprehend their environment, which promotes safe navigation and well-informed decision-making. Even with these advancements, present-day perception algorithms sometimes struggle to remain dependable under harsh weather situations like dimly lit rooms, intense sunlight, reflecting glare, or obscuring fog. These circumstances have the potential to seriously impair perception system performance, raising safety concerns. As we move closer to the objective of complete autonomy, it is critical that we create a robust perception system--a system that can confidently manage these various and demanding external circumstances. With the help of such a system, autonomous cars will be able to function consistently safely and precisely, even in unpredictable real-world situations. Achieving this degree of perception system complexity is not just a goal, but also a prerequisite for the development of fully autonomous cars that have the potential to completely transform the way we travel. Our study uses the CNN method, which is a powerful tool for deep learning, to take on this difficulty head-on. The CNN is being trained to be exceptionally good at identifying cars in bad circumstances, such as fog and rain. The objective is to provide our system with the capacity to surpass the constraints of hazy images and accomplish high-fidelity, real-time vehicle recognition. This results in a notable advancement in AV safety, guaranteeing their ability to confidently traverse even the most difficult weather situations. We're laying the groundwork for a time when autonomous cars will be able to drive on roads with greater safety and efficiency in all weather conditions by fusing the capabilities of deep learning and WDS. In this research, we are detecting vehicles in inclement weather by utilizing an algorithm based on YOLO.