Vehicle Classification and Accident Avoidance in Hill Station ising CNN Algorithm

Vehicle classification and accident avoidance in hill stations is an important problem that can be solved using computer vision techniques and machine learning algorithms. The proposed system consists of an intelligent camera system that can classify vehicles based on their type and detect potential collisions with other vehicles or obstacles on the road. The system is built using a Convolutional Neural Network (CNN) algorithm for vehicle classification, (RF) encoder and decoder for feature extraction, and an Arduino Uno microcontroller for Alerting the vehicle. In the existing system, vehicle classification and accident avoidance are typically done manually by the driver, which is not always reliable, especially in hilly terrain. The proposed system overcomes this limitation by automating the process using advanced computer vision and deep learning algorithms. The proposed system uses a web camera using vehicle images in the mountain to capture real-time images of the road ahead. Then processed using a CNN algorithm for vehicle classification, which can identify various types of vehicles, such as cars, buses. The system uses an RF encoder and decoder to extract features from the images and classify the vehicles accurately. Once a vehicle is classified, the system checks for potential collisions with other vehicles or obstacles on the road. If a potential collision is detected, the system sends a signal to the Arduino Uno microcontroller, which alert the vehicle to avoid the collision.