Every country has a significant problem with road connectivity in terms of development. The road damage assessment along particular thoroughfares has revealed that governmental efforts to preserve road quality whether through construction or maintenance are substantial. Potholes are typically the first thing noticed while assessing roadway quality in developing nations such as India. Intelligent Transportation Systems (ITS) have made significant strides in automation and computer vision compared to other methods such as radar, sensor bases, manual methods, etc. Recent research has shown that intelligent transport systems perform exceptionally well, especially in pothole detection and assessment. Recent advances in artificial intelligence, particularly machine learning and deep learning, have advanced robotics and automation. Modern technology has produced better results in production and cost-efficiency than traditional methods. The paper emphasizes the need for better road maintenance to decrease accidents caused by potholes on Indian roadways. Intelligent Transportation Systems (ITS) face road abnormalities beyond road damages that pose safety issues. Detecting and managing surface fissures, potholes, road signs, landslides, and animal crossings are the concerns. Deep learning and artificial intelligence can improve Intelligent Transportation Systems by providing a holistic approach to road concerns. Technology should make transportation networks safer and more efficient. The research aims to examine the effectiveness of three deep-learning object identification frameworks (YOLOv5, YOLOv6, and YOLOv7) in detecting potholes. The results demonstrate that deep learning methods are highly effective for identifying road damage within the Intelligent Transport System, especially potholes. The dataset included in this study consists of photographs depicting potholes observed on diverse categories of roadways, namely municipal, state, and national highways. The empirical findings indicate that YOLOv7 has superior efficiency as a pothole detector, with a precision rate of 93%.


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