Abstract:

Visibility is greatly lowered by atmospheric phenomena including rain, fog, and haze, especially when bad weather is present. Because they can cause accidents and impede drivers' eyesight, these weather conditions pose a serious risk to road safety. Rain, fog, and haze may be identified and eliminated from photos taken by traffic cameras or sensors installed on vehicles to greatly improve visibility and assist drivers avoid collisions. Conventional systems frequently rely on filters and image processing methods. These techniques aim to improve vision by lessening the effect of haze, fog, and rain streaks on pictures. Nevertheless, the efficacy of these conventional methods is sometimes restricted, particularly in situations involving real-time applications and fluctuating weather conditions. For this reason, having a reliable system in place for clearing haze, rain, and fog is crucial for traffic safety. When combined with efficient removal methods, accurate and timely identification of certain meteorological conditions can improve road visibility, lower the risk of accidents, and even save lives. These devices are particularly important for driverless cars, as safe navigation depends on having clear, unhindered vision. Consequently, the goal of this project is to construct a system using OpenCV, a well-known open-source computer vision toolkit. Developing rain, fog, and haze detection and removal systems is made easier using OpenCV's extensive feature set and array of methods. To tackle these issues, it provides a range of image processing methods, such as morphological operations, filtering, and machine learning algorithms. In order to reduce road accidents during bad weather, intelligent algorithms may be used in conjunction with OpenCV to produce precise and efficient solutions for real-time applications.

Description:

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