Realtime Detection of Humps and Potholes

Hump and pothole detection is essential for ensuring road safety and preventing damage to vehicles. In recent years, there has been a growing interest in developing automated methods for hump and pothole detection. This paper presents the detection of humps and potholes using techniques of image processing, machine learning and sensor-based approach. The proposed method involves the use of cameras and sensors to collect data on road conditions, analyze the data and identify areas with humps or potholes. Machine learning algorithms are applied learn to recognize patterns in the data and make accurate predictions. The solution provided is robust with an accuracy of 90% approximately in achieving the objective under different lighting and weather conditions. The proposed solution is implemented in real time and tested for different conditions. The analysis is carried out in terms of improved road safety and reduced maintenance costs using the proposed solution.