“智路巡守”——基于众包与专业数据融合的道路网智能巡检平台研究报告

In order to overcome the uncertainty of the traveling routes of commercial road inspection vehicles and the incompleteness of the road inspection coverage in a fixed area, this study develops a road network intelligent inspection platform based on crowdsourcing and professional data fusion. The platform utilizes the visual recognition equipment equipped on commercial vehicles to inspect the road network, and generates the optimal inspection routes through the Google Or-Tools VRP solver; uses the YOLO11 algorithm to determine the severity of road damage, and accordingly divides the repair priority of road sections and generates an intelligent maintenance program; generates a regular inspection report, and carries out detailed statistics and reports on the inspection situation and pavement damage data during the cycle. The platform provides optimized inspection routes, which reduces the frequency of inspection vehicles, improves inspection efficiency and enhances the response speed of pavement maintenance. The platform relies on the inspection frequency of commercial vehicles, and the inspection effect may be reduced when there are fewer commercial vehicles. This study provides an efficient solution for intelligent inspection of road networks, which helps to improve the rapidity and scientificity of road maintenance and reduce the cost of road maintenance inspections.