Road Surface Detection Using R-CNN Vision Models
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Solution Overview
Problem
Current systems for providing road surface information to drivers are limited by their reliance on smartphone applications, GPS, and camera images, lacking real-time quantitative detection and effective guidance for safe driving on uneven surfaces.
Innovation Solution
A road surface detecting apparatus and method using a camera to obtain images, a processor to classify road surface events through a generated detection model, and a region convolution neural network (R-CNN) for pre-processing and training, enabling real-time detection and guidance on road conditions such as protrusions and recesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smartphone-based application using GPS information and vehicle mounted camera is used to provide road surface information, then the system can provide basic road surface information, but the measurement precision and real-time quantitative detection capability are limited
Solution Approach 1:
The patent replaces traditional mechanical and manual road surface detection methods (smartphone apps, GPS, basic cameras) with an automated vision-based detection system using a camera and deep learning algorithm (R-CNN). This substitution enables precise, real-time quantitative detection of road surface events without requiring manual intervention or complex mechanical sensors, thereby improving measurement precision while maintaining manageable system complexity.
Solution Approach 2:
The patent transforms road surface information from qualitative (basic visual assessment) to quantitative detection by using the R-CNN algorithm to detect and classify specific road surface events. The system changes the parameter representation from general location data to precise detection results including event type, position, and characteristics, enabling real-time quantitative analysis of road conditions.
2Reliability
If real-time quantitative detection of road surface information is implemented, then safe driving guidance can be provided, but the device complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical sensor systems with a vision-based detection system using a standard camera and software-based image processing. The R-CNN algorithm performs real-time quantitative detection of road surface events, providing reliable safe driving guidance through software intelligence rather than complex hardware, thereby improving reliability while keeping device complexity manageable.
Solution Approach 2:
The patent uses a camera to capture optical copies (images) of the road surface, which are then processed by the R-CNN algorithm to extract quantitative information about road surface events. This copying approach allows real-time detection and analysis without requiring direct physical contact with the road surface, enhancing reliability while avoiding the complexity of mechanical measurement systems.
3Measurement precision
If image processing and model training are performed to detect road surface events, then detection accuracy improves, but the processing time and computational energy increase
Solution Approach 1:
The patent performs preliminary action by training the R-CNN detection model in advance using labeled road surface images. This pre-training phase creates a ready-to-use detection system that can quickly classify new road surface events in real-time. The computationally intensive model training is done beforehand, allowing fast inference during actual road surface detection without significant processing delays.
Solution Approach 2:
The patent uses software-based deep learning (R-CNN algorithm) to perform detection, replacing slower traditional image processing methods. The algorithm automatically learns features from training data and performs rapid classification of road surface events, achieving high detection accuracy with reduced processing time compared to conventional computer vision approaches.
Data Source
AI summary
A road surface detecting apparatus may include a camera configured to obtain an image of a front side of a vehicle including a road surface, and a processor configured to classify a road surface event based on a road surface detection model which is generated by performing training based on the obtained image. Accordingly, a stable riding feeling may be provided for the user by quantitatively detecting a road surface event through training and controlling the vehicle based on the detected road surface event.