Automated speed breaker system using IoVT generated data for Electric Vehicle using Machine Learning

The identification of speed breakers on roads is crucial for ensuring a safe and comfortable driving experience for motorists. In this paper, we present an approach to identify speed breakers using Artificial Intelligence (AI) techniques. The proposed approach is based on processing images captured by cameras mounted on vehicles or drones, which are commonly used for traffic monitoring and surveillance. The approach involves several steps, including image preprocessing, feature extraction, and classification. In the preprocessing step, images are enhanced to improve the quality and clarity of the speed breaker features. Feature extraction is performed using computer vision algorithms to extract relevant features such as shape, size, and color of the speed breakers. Finally, a classification model is trained using machine learning algorithms to classify the extracted features as speed breakers or non-speed breakers. To evaluate the performance of the proposed approach, we conducted experiments using a dataset of images collected from different roads. The results show that our approach achieved high accuracy and precision in identifying speed breakers, with an overall accuracy of over 90%. The proposed approach has potential applications in traffic management and road safety. By identifying speed breakers accurately and efficiently, road authorities can take appropriate measures to maintain the road infrastructure, improve driving conditions, and reduce accidents. Furthermore, the approach can be extended to identify other road features such as potholes, pedestrian crossings, and traffic signals, which can further improve road safety and traffic management.