Implementation of Fatal Vehicle Collision Avoidance System for the Deterrence of Mishaps in Potential Zones

Road traffic sign detection is an important task for ensuring road safety. In our day today life, time is very important thing so everyone trying to complete the task in less time is the human tendency. Therefore to complete the desired task as early as possible we should increase the speed, for example speed of vehicle. Road traffic sign detection is a challenging task that has been the subject of research for many years. The detection of road signs is essential for ensuring the safety of drivers, pedestrians, and other road users. As well as the drivers are not following the rules and regulation given by traffic control department at specific areas. But most of the drivers drive the vehicle very fast in that restricted areas with and without reasons. Existing systems for road traffic sign detection often rely on traditional computer vision techniques, such as template matching and feature extraction. These techniques are limited in their ability to detect signs in different lighting conditions and may not be able to identify signs with occlusions. Moreover, existing systems may not be capable of detecting different types of zones, such as school zones, hospital zones, and accident zones. The proposed system uses a CNN algorithm for real-time road traffic sign detection. The system consists of three main stages: image acquisition, sign detection, and sign classification. In the first stage, the system acquires an image of the road scene using a camera. In the second stage, the system uses image processing techniques to detect road signs in the acquired image. The system then extracts features from the detected signs and uses a CNN algorithm to classify them according to the three different types of zones: school zones, hospital zones, and accident zones. The proposed algorithm can be used as an effective tool for real-time road traffic sign detection, particularly for detecting school zone, hospital zone, and accident zone signs. The algorithm's accuracy and efficiency make it suitable for use in various applications, including autonomous driving, traffic monitoring, and road safety.