## 1 Introduction  
Underground pipe networks play a crucial role in the daily operation of cities. As part of the urban municipal system, they cannot be ignored in the process of urban intelligent digitization. With the continuous development of smart cities and the continuous enrichment of urban functions, the process of municipal public facilities construction is gradually accelerating. This means that municipal, power, communications, and other departments need more practical measures to manage a large number of municipal equipment and assets. At present, many old urban areas have a lack of information on underground pipelines, which makes it impossible to carry out intelligent management of pipelines. In turn, the precise lack of access to underground pipelines is a challenge for urban infrastructure management. Although a variety of techniques, such as ground penetrating radar (GPR), acoustic detection, stratigraphic detection, geomagnetic detection, and holographic interferometry, exist for the detection of underground pipe networks [1, 2, 3]. These methods usually require a large investment of labor and resources, which limits their efficiency and usefulness. These traditional methods are labor intensive and often lead to excessive consumption of time and cost. Manhole covers, serving as critical connectors between the underground pipeline network and the surface, play an essential role; thus, mapping the layout of the underground network can be achieved by acquiring information on the type and location of manhole covers. As artificial intelligence technology progresses and the use of unmanned aerial vehicles (UAVs) becomes more widespread in engineering applications, more and more deep learning techniques are being leveraged to solve engineering-related problems [4; 5; 6]. For example, many researchers have applied object detection algorithms to the task of locating manhole covers. [PERSON] [7] and colleagues had introduced a method termed MA-FPN, which integrated the core concepts of attention mechanisms with feature pyramid networks, aiming to improve the accuracy of object detection in remote sensing. [PERSON] et al. [8] designed a diversity feature extractor based on a VGG feature extractor to improve the performance of manhole cover localization by increasing the size of the receptive field. These methods obtained the city image by using UAV aerial photography, and then used the object detection technology to detect the aerial image to obtain the localization of the manhole cover. It greatly reduced the consumption of manpower and material resources, and also improved the survey efficiency. However, these methods had not solved the problem of obtaining the category information of manhole covers, and could only obtain the location information of manhole covers. However, in the process of urban digitization, the category information of manhole cover is indispensable. Only by correctly obtaining the category information of manhole cover can we build a complete municipal pipe network system. Other scholars have proposed using vehicles equipped with cameras and lidars to collect ground images and combine target detection methods to obtain manhole cover information [9; 10]. For example, [PERSON] et al. [11] used a combination of multiple symmetrically arranged cameras and lidars to classify manhole covers through descriptors and support vector machine algorithms. [PERSON] et al. [12] proposed a real-time road manhole cover detection method based on deep learning model. By optimizing the network structure and reducing the size of the model and the number of parameters, the effect of deployment on vehicle-mounted embedded devices is achieved. [PERSON] et al. [13] developed a fully automatic method for manhole cover detection using mobile mapping point cloud data. Although the above method can solve the problem of manhole cover classification, due to the limitation of vehicle-mounted tools, a large number of manhole covers are located in places where cars cannot reach.