Objective The safety of pavement manhole covers is crucial in urban development. The timely and accurate detection of manhole cover disease can save maintenance costs, reduce road hazards, and ensure driving safety. Traditional methods for surveying and mapping manhole covers mainly use manual measurements, which usually require considerable human and material resources. Moreover, such measurements have a low operating efficiency and poor safety, which is not conducive for the rapid update of data. Therefore, new, efficient, and automated methods and techniques are urgently required for the manhole cover measurement and disease detection. Currently, methods for manhole cover extraction and disease detection mainly include the differential polar method, ellipse feature-based fitting algorithm, and image feature detection method. These methods exhibit low robustness. Moreover, the direct image detection methods are affected by the image quality and illumination. It is difficult to obtain information about manhole cover diseases using such methods. This study develops a technical process involving the original point cloud and the manhole cover extraction and disease detection using vehicle-borne laser point cloud data. Based on the intensity image combined with the improved Hough algorithm for achieving the accurate road manhole cover position and disease information, the experimental results show a good robustness and stability of the proposed method. We hope that the proposed technical solution can help the city management department in inspecting and maintaining manhole covers to effectively improve the extraction efficiency and operation safety of manhole covers.

Methods First, based on the high-precision vehicle-borne laser point cloud data, accurate ground point cloud data were obtained using a combined filtering algorithm of the point cloud gradient and cloth simulation. They eliminate the influence of invalid features on the manhole cover extraction. Second, the intensity orthographic method was used to generate high-resolution intensity images of the ground points. Moreover, the manhole cover was binarized using the adaptive threshold method to increase the edge display effect of the road manhole cover. Then, according to the shape and position characteristics of the manhole cover circle, the edge was detected based on the image binarization segmentation result. Further, the location of the manhole cover circle was divided into potential manhole cover object detection and a real manhole cover using the Hough circle detection algorithm, which strictly limits the curvature and edge accumulation threshold. Finally, using the two processes of object detection, the precise extraction of the manhole cover position was achieved. Next, the disease information of the manhole cover was obtained by calculating the elevation value of the adjacent point cloud within a certain distance between the manhole cover position and the surrounding area. Finally, a high-precision GPS-RTK and DS3 level comparison experiment was performed to evaluate the stability and reliability of the proposed algorithm.

Results and Discussions Regarding the road surface properties of the manhole cover position, this study first proposes the combined filtering method of the point cloud gradient and cloth simulation. The latter performs secondary filtering to retain and optimize the ground point results of gradient filtering for invalid floating data elimination. Several data tests were used to obtain the accurate ground point cloud results (Fig.12). Because the intensity image contains considerable noise and requires a large number of Hough calculations, the accurate position of the manhole cover circle is obtained by detecting the edge contour (Fig.13) and setting the appropriate curvature and edge accumulation threshold in the improved Hough circle detection algorithm. Combined with the actual vehicle-borne laser point cloud experimental study, the accuracy and precision of manhole cover extraction reach 84% and 98%, respectively. Additionally, the manhole cover extraction efficiency is significantly improved and the vectorization result of the manhole cover extraction can be displayed in the real point cloud coordinates (Fig.14). Furthermore, the accuracy experimental results show the robustness and reliability of the manhole cover extraction plane position (Table 2) and settlement disease detection results (Table 3).

Conclusions In this study, in view of the difficulty and low efficiency of traditional road manhole cover measurements, the vehicle-borne laser point cloud data are directly used to achieve the precise manhole cover position and disease detection. First, a combined filtering algorithm of the point cloud gradient and cloth simulation is proposed to obtain the high-precision ground point data and generate intensity images. Then, this technique was combined with the adaptive threshold binarization method to obtain the high-discrimination manhole cover edge contour using the improved Hough circle detection algorithm. The manhole cover is approximately positioned within the circle curvature limit, and the accurate position is achieved using the edge accumulation threshold based on the previous step. Finally, the position parameters and disease information of the manhole cover are obtained. Combined with field experiment data verification, the accuracy and precision rate of manhole cover extraction of the proposed method reach 84% and 98%, respectively, greatly improving the manhole cover disease detection efficiency and operation safety compared with traditional methods. Combined with the precision analysis of the same name detection points, the manhole cover extraction results of the proposed scheme show high accuracy and the data results can meet the requirements of related projects. The technical scheme and experimental results of this research show the effectiveness and reliability of the vehicle-borne laser point cloud used in manhole cover extraction and disease detection and provide new ideas for urban intelligent management.