The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Abstract

Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

“…For example, the ground sample distance, which is crucial in determining the detail captured in imagery from UAVs, is dependent on both the camera's resolution and the altitude of the flight [98]. The increased resolution in UAV imagery is crucial for accurately identifying potholes on road surfaces, facilitating timely maintenance and management decisions [99]. It is these variations in resolution and ground sample distance which influence the choice of technology based on the required accuracy and the nature of the terrain or project at hand.…”

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

“…Furthermore, research aimed at enhancing efficiency in architecture and urban management through machine learning includes studies on various approaches. These include investigations into detecting road potholes and their locations [18]; comparing the effectiveness of machine learning algorithms such as support vector machine (SVM), Maximum Likelihood (ML), and Random Trees (RT) for extracting impervious surfaces in residential complexes and illustrating their spatial distribution [19]; and developing a machine learning technique called Building Detection with Shadow Verification (BDSV) based on high-resolution satellite images to automatically detect buildings within urban areas [20]. These studies explore the applicability of machine learning in the field of architecture and urban planning and contribute to solving relevant problems.…”