Authors - Aneesh Kaleru, Chaitanya Neredumalli, Mrudul Reddy, Ramakrishna Kolikipogu Abstract - One major risk factor that contributes to traffic accidents globally is poor visibility in foggy situations. Drivers are seriously threatened by fog because it weakens contrast, hides important objects, and makes lane markings almost invisible. Recent developments in visibility enhancement methods for foggy circumstances are summarized in this paper, with a focus on picture defogging combined with object detection and lane aid. We analyze the application of models such as Conditional Generative Adversarial Networks (cGANs), Single Shot Multibox Detectors (SSD), All-in-One Defogging Network (AODNet), and You Only Look Once (YOLO) from the perspective of deep learning and computer vision. These methods have the potential to increase driver safety in inclement weather by identifying impediments, improving visibility, and offering lane guidance. The review also covers the limitations of these solutions, such as computational demands and requirements for real-time processing. Our goal is to provide researchers and practitioners with a comprehensive understanding of the current methods and their uses, allowing for the development of effective visibility enhancement systems that can prevent accidents and save lives.
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