Research on Road Surface Inspection System for Lightweight Design
DOI:
https://doi.org/10.6919/ICJE.202505_11(5).0036Keywords:
Lightweight Design; Road Surface Disease; Industrial Camera; Satellite Positioning; YOLOv8n; Road Surface Maintenance.Abstract
Given the problems of low efficiency, heavy workload, and hidden safety problems associated with manual inspection, this paper proposes a multi-sensor fusion road inspection system for lightweight design. The system adopts a high-definition industrial camera and GNSS / IMU joint positioning terminal to realize the accurate acquisition of high-definition images of roads and real-time location data. At the same time, this study uses the lightweight disease detection model to optimize the inspection process and improve coordination efficiency. Acquisition application results show that the system in common cracks, pits, and other pavement disease detection realizes the rapid, precise data acquisition and, through the task management function, significantly improves the inspection efficiency and data accuracy for the road management department and maintenance unit provides timely and accurate maintenance data support, promote the road maintenance of intelligent management level.
Given the problems of low efficiency, heavy workload, and hidden safety problems associated with manual inspection, this paper proposes a multi-sensor fusion road inspection system for lightweight design. The system adopts a high-definition industrial camera and GNSS / IMU joint positioning terminal to realize the accurate acquisition of high-definition images of roads and real-time location data. At the same time, this study uses the lightweight disease detection model to optimize the inspection process and improve coordination efficiency. Acquisition application results show that the system in common cracks, pits, and other pavement disease detection realizes the rapid, precise data acquisition and, through the task management function, significantly improves the inspection efficiency and data accuracy for the road management department and maintenance unit provides timely and accurate maintenance data support, promote the road maintenance of intelligent management level.
Given the problems of low efficiency, heavy workload, and hidden safety problems associated with manual inspection, this paper proposes a multi-sensor fusion road inspection system for lightweight design. The system adopts a high-definition industrial camera and GNSS / IMU joint positioning terminal to realize the accurate acquisition of high-definition images of roads and real-time location data. At the same time, this study uses the lightweight disease detection model to optimize the inspection process and improve coordination efficiency. Acquisition application results show that the system in common cracks, pits, and other pavement disease detection realizes the rapid, precise data acquisition and, through the task management function, significantly improves the inspection efficiency and data accuracy for the road management department and maintenance unit provides timely and accurate maintenance data support, promote the road maintenance of intelligent management level.
Given the problems of low efficiency, heavy workload, and hidden safety problems associated with manual inspection, this paper proposes a multi-sensor fusion road inspection system for lightweight design. The system adopts a high-definition industrial camera and GNSS / IMU joint positioning terminal to realize the accurate acquisition of high-definition images of roads and real-time location data. At the same time, this study uses the lightweight disease detection model to optimize the inspection process and improve coordination efficiency. Acquisition application results show that the system in common cracks, pits, and other pavement disease detection realizes the rapid, precise data acquisition and, through the task management function, significantly improves the inspection efficiency and data accuracy for the road management department and maintenance unit provides timely and accurate maintenance data support, promote the road maintenance of intelligent management level.
Downloads
References
[1] Yan Yufang. Analysis of intelligent road maintenance under intelligent transportation [J]. Smart Building and Smart City, 2024 (05): 180-182.
[2] Lu Fengbin, Gai Yingde, Cui Zhenyu, et al. An intelligent automatic inspection system based on UAV: CN202011141984.7 [P]. CN112327906A
[3] Fan Jiaqian, Wang Zewen, Yang Rui, et al. The Application of the nondestructive testing technology of the vehicle-mounted road structure in the intelligent operation and maintenance of the factory area [J]. Highway and Transportation in Inner Mongolia, 2023 (05): 7-10.
[4] Zimmer W , Birkner J , Brucker M ,et al.InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors[J].ArXiv, 2023, abs/2305.00314.66
[5] Cavnue Company. Sensor project [EB/OL] implemented on the Michigan I-94 Highway. https://time.com/7094823/cavnue- connected-and-automated-vehicle-corridor/.
[6] Liu Hao, Cao Wanghui, Tang Feng. Design and application of daily inspection system for lightweight vehicle roads [C]// China Intelligent Transportation Association. Science and technological proceedings of the 16th China Intelligent Transportation Annual Conference. Shanghai Lishi Technology Co., Ltd.; 2021:99-107.
[7] Zhu Xiaofeng. Research on intelligent detection system of foreign body on vehicular expressway pavement [D]. Fujian Agriculture and Forestry University, 2024.
[8] Qin Qing Fei. Intelligent garbage pick-up and road event inspection system in the central isolation zone of the expressway [J]. Automation application, 2019 (05): 15-16.
[9] Chenglin C, Fei W, Min Y,et al. An efficient lightweight railway track segmentation network for resource constrained platforms with TensorRT[J]. Intelligent Transportation Infrastructure, 2024.
[10] Regular light exposure. Analysis of on-board lightweight road intelligent inspection system [J]. China Security, 2020 (9): 100-104.
[11] Li X , Zhou F , Du J .LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks[J].IEEE Transactions on Information Forensics & Security, 2013, 8(6):924-935.
[12] Arya D , Maeda H , Ghosh S K ,et al.Transfer Learning-based Road Damage Detection for Multiple Countries[J]. 2020.
[13] Arun Balaji P , Naveen Venkatesh S , Sugumaran V ,et al.Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN[J]. Advances in Mechanical Engineering (Sage Publications Inc.), 2024, 16(6).
[14] Yan Canze, Li Zhao. Highway maintenance and inspection technology and application based on autonomous vehicles [J]. Smart City, 2024,10 (01): 5-8.
[15] Xu Xin, Ma Yadong, Fan Ting, et al. Intelligent inspection method of road state perception based on "Beidou + on-board video recognition" [J]. China Highway, 2023 (24): 114-117.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.



