AI Road Risk Detection System for Surface Defect Recognition
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Solution Overview
Problem
Conventional navigation systems fail to provide intuitive guidance on road conditions, leading to increased accident risk when traffic lanes or road surface markings are obscured or damaged, as they treat roads as simple lines without considering actual road states.
Innovation Solution
A deep learning-based system that collects road state information, performs image processing to create grayscale images, and classifies road risk using a learning model to detect surface defects, enhancing reliability and preventing accidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional navigation systems treat roads as simple lines for route guidance, then route navigation function is simple and efficient, but the system cannot provide intuitive guidance on actual road conditions such as obscured markings or surface defects
Solution Approach 1:
The patent replaces conventional mechanical/navigation-based road representation with an AI-based visual recognition system. The navigation system uses deep learning models to process camera images and automatically detect road surface defects, obscured markings, and traffic conditions, substituting the simple line-based road model with intelligent visual analysis that provides reliable road condition information without requiring complex manual intervention
Solution Approach 2:
The system enables self-service through automated AI processing of road condition data. The deep learning model automatically analyzes captured images, identifies road defects and marking visibility without human intervention, and integrates this information into navigation guidance. This self-service capability allows the system to reliably assess road conditions while maintaining operational simplicity
2Loss of information
If the navigation system adds road state detection capability using deep learning, then the ability to provide practical road condition guidance is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies preliminary action by pre-training deep learning models with extensive road condition data before deployment. The models are pre-trained to recognize various road defects, marking patterns, and traffic conditions, enabling rapid real-time inference during actual navigation without requiring complex processing during runtime. This preliminary preparation reduces processing time while maintaining complete road state information
Solution Approach 2:
The patent segments the road condition analysis into distinct processing stages: image capture, preprocessing, feature extraction by the deep learning model, and result integration. This segmentation allows the system to process only relevant visual information efficiently, reducing overall processing time while maintaining comprehensive road state detection across different road conditions
3Measurement precision
If the system processes and classifies road risk information using AI learning models, then the accuracy of road surface defect detection is improved, but the computational power required increases
Solution Approach 1:
The patent replaces energy-intensive traditional image processing methods with optimized deep learning models that achieve higher detection precision with reduced computational overhead. The AI models are specifically designed to efficiently process road condition images, identifying defects and markings with high accuracy while consuming less energy than conventional computer vision approaches
Solution Approach 2:
The system applies parameter changes by optimizing the deep learning model architecture and processing parameters for mobile deployment. The model uses adjusted input image resolutions, optimized neural network layers, and efficient inference parameters that maintain high detection precision while significantly reducing computational energy consumption during real-time road condition assessment
Data Source
AI summary
Provided is an artificial intelligence system for providing road risk information and a method thereof. The system for providing road risk information includes: an information collection unit for receiving various road state information acquired from a vehicle device; an information processing unit for performing image processing on the collected road state information, and converting a result of the image processing into a predefined grayscale image; an information learning unit for learning the converted predefined grayscale image on the basis of a predetermined learning model based on deep learning, and recognizing the road risk information on the basis of a result of the learning; and an information classification unit for classifying the road risk information from the road state information on the basis of a result of the recognition, and detecting road surface defects on the basis of a result of the classification.