Work

Development and Application of an AI-Based Automatic Identification System for Rural Road Distress and Maintenance Management

Year: 2025

Type: article

Abstract: With the continuous expansion of rural road construction and increasing management demands, traditional rural road inspection and maintenance models are becoming insufficient to meet current needs. The analysis of inspection results and the development of maintenance plans are often delayed. To address these challenges, this paper proposes a rural road distress sample recognition and annotation method based on machine vision techniques, and establishes a corresponding disease target identification sample database. The method is trained and validated using the U-Net algorithm, achieving an accuracy of 94.95%. Additionally, a lightweight detection system is developed to facilitate rural road surface disease target detection and automatic recognition. The self-developed automatic recognition system significantly enhances the accuracy and efficiency of pavement disease recognition. Furthermore, a management platform has been implemented to enable the dynamic management of rural road disease data and maintenance operations.

Source: Buildings

DOI: 10.3390/buildings15234222

Authors: Longjin Chen, Hang Zhang, Dongfang Li, Yulong Li, Jiang Lou, Kenxuan Fu

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137

Funder: Department of Transportation of Zhejiang Province