Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation

2026-04-27

AI Summary

This study addresses the accuracy and efficiency bottlenecks in vehicle-based crack detection under real-world road conditions, which are primarily caused by image redundancy and communication constraints. To overcome these limitations, this work proposes the first infrastructure-guided, communication-enhanced collaborative detection framework. The approach employs a customized roadside-to-vehicle communication protocol to transmit regions of interest, integrates dynamic image cropping and keyframe selection to optimize input data, and leverages an advanced detection model trained on a forward-looking road crack dataset. For the first time, the system enables effective collaboration between roadside infrastructure and onboard units for crack identification. Experimental validation on a real vehicle platform demonstrates significant improvements in detection accuracy and practical deployability.

Technology Category

• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

In this paper, we report the world's first infrastructure-guided communication-enhanced road crack detection pipeline that is effective and implementable on passenger vehicles. We first design a customized communication protocol to transmit the region of interest from the infrastructure to the vehicle. With proper camera image processing (e.g., dynamic cropping and frame selection), the focused images are provided to the crack detection model. Leveraging state-of-the-art crack detection model backbones and a carefully prepared dataset comprising a forward-facing view with a crack, we train the model to improve crack-detection performance. We demonstrate the full detection pipeline on an experimental vehicle platform, showcase the detection effectiveness, and project future research directions.

Problem

• road crack detection
• infrastructure-guided
• connectivity-enhanced
• vehicle-based sensing
• crack estimation

Innovation

• infrastructure-guided
• communication-enhanced
• road crack detection
• region of interest transmission
• dynamic cropping