Autonomous UAV Pothole Detection With Repair Prioritization
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Solution Overview
Problem
Conventional road inspection for potholes is manual, labor-intensive, time-consuming, and resource-intensive, requiring significant time and resources for periodic or complaint-driven inspections.
Innovation Solution
A system utilizing autonomous unmanned aerial vehicles (UAVs) equipped with video cameras, video processors, and global positioning receivers to detect potholes, generate geo-fenced areas, and transmit data to a cloud server for processing, classification, and repair prioritization using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual road inspection is conducted by human inspectors, then road deterioration can be detected, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical inspection by human inspectors with an automated system using UAVs equipped with video cameras and deep convolutional neural networks for pothole detection. This substitution eliminates the need for human traversal and manual assessment, significantly reducing inspection time while maintaining or improving detection accuracy through algorithmic analysis of road surface images.
2Measurement precision
If manual road inspection is conducted by human inspectors, then road deterioration can be detected, but significant resources are required
Solution Approach 1:
The system replaces human inspectors and manual inspection resources with autonomous UAVs that can operate independently. The deep learning models process video data automatically, eliminating the need for human time and expertise. This substitution dramatically reduces resource consumption while achieving consistent, scalable detection across multiple road segments simultaneously.
Solution Approach 2:
The UAV-based system performs self-service inspection by autonomously navigating road areas, capturing video data, processing images through embedded neural networks, and generating detection results without human intervention. This self-service capability eliminates dependency on human inspectors and reduces operational resource requirements.
3Reliability
If periodic road inspection is conducted, then road safety can be monitored, but the frequency is limited by time and resource constraints
Solution Approach 1:
The automated UAV inspection system enables increased inspection frequency by replacing slow manual processes with fast, autonomous aerial surveys. Multiple UAVs can operate in parallel, covering extensive road networks in shorter timeframes, thereby allowing more frequent monitoring intervals while maintaining or improving road safety through timely detection of potholes and deteriorations.
4Measurement precision
If comprehensive road inspection is performed, then all potholes can be detected, but the complexity and time required increase significantly
Solution Approach 1:
The patent employs deep convolutional neural networks embedded in UAVs to automatically analyze road surface images and detect potholes. This automated image processing system comprehensively scans entire road segments captured in video footage, ensuring complete detection without the subjectivity and fatigue limitations of human inspectors, while the algorithmic approach manages complexity through standardized processing pipelines.
Data Source
AI summary
A pothole monitoring and repair system for a road surface includes at least one UAV which includes a video camera, a video processor, a computing unit and a global positioning receiver. The UAV generates video streams of road surfaces using the video camera which are processed by the video processor to extract road frames. The computing unit generates a geo-fenced area of the road surface, and determines whether there is at least one pothole in each of the road frames within the geo-fenced area. The cloud server generates a map of the geo-fenced area, labels location coordinates of each pothole on the map, identifies and labels geographical features in the road frames within the geo-fenced area, extracts pothole features in the road frames, and uses a classifier to predict repair actions based on pothole features and geographical features, which are presented on a front end interface.