Pothole Detection System Using Region-Based Convolutional Neural Network
Aaquib Javed, Md. Sayem Mahmud, Md Takbir Alam, Md. Foysal Bin Ohab, Khandakar Ratul Ali, Abdullah Al Jobaer, M. Monir Uddin
Abstract
Street surface weakening, for example, potholes, has caused drivers substantial money-related harm each year. Notwithstanding, viable street condition observing has been a proceeding with a challenge to street proprietors. Profundity cameras have a small field of view and can be effectively influenced by vehicle bobbing. Customary picture handling strategies are dependent on calculations. For example, the division can't adjust to shifting ecological and camera situations. In this paper, the object detection API for pothole detection is used to test the set of images and videos and give the output results of the tested images and videos. By evaluating the R-CNN algorithm and SSD mobile net algorithm, the results of the test showed successful results in getting potholes from test images with a maximum confidence level of 93%.