INTELLIGENT POTHOLE DETECTION AND ASSESSMENT SYSTEM: AREA AND DEPTH ESTIMATION

Abstract

Economic and social prosperity relies heavily on well-developed and maintained highways, but lack of funding and resources makes highway maintenance difficult. Potholes and early road deterioration pose significant risks for automobile accidents. Therefore, it is essential to identify and repair potholes promptly to maintain dependable and safe road infrastructure. To address this issue, the YOLO(You Only Look Once) detection algorithm is used to detect potholes in this work. The proposed approach includes a Single Pothole Detector based on the YOLO-v1 Algorithm and a Multiple Pothole Detector based on the YOLO-v3 and YOLO-v5 Algorithms. Various backbone architectures are utilized to identify potholes in the Single Pothole Detector experiment. In addition, attention mechanisms are integrated into these backbone architectures to improve performance in pothole detection, and computer vision methods are employed to estimate the area of potholes. The models are trained and validated using a Custom dataset comprising the MakeML pothole dataset, a custom dataset from Kaggle, and two real-time footage of Kerala roadways. Additionally, Depth estimation is being carried out using Encoder-Decoder architecture. The Pothole Detectors’ performance is evaluated using mean Average Precision (mAP) and Average Precision(AP) metrics to compare them with other models. The results show that the proposed Pothole Detectors perform better than other models, highlighting their potential in identifying and addressing potholes promptly to maintain safe and dependable road infrastructure.