Camera-Based Pavement Condition Estimation Using Cross-Instrument Training
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Solution Overview
Problem
Existing image-based methods for estimating pavement conditions are often slow, expensive, and require significant human intervention, specific lighting, and camera orientation, making them inadequate for real-time and cost-effective applications in roadway and airport operations.
Innovation Solution
Systems and methods that label data from one instrument based on data from another instrument, allowing for the training of a computer learning model without specific feature extraction, enabling the estimation of pavement conditions from camera images without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based methods are used to estimate pavement conditions, then measurement precision can be improved, but device complexity and operational requirements increase significantly
Solution Approach 1:
The patent introduces a computer learning model as an intermediary that bridges the gap between simple camera observations and complex pavement condition analysis. The model learns to map image data directly to pavement conditions without requiring complex feature extraction pipelines, lighting control systems, or manual annotation processes, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent replaces traditional mechanical image processing systems with a data-driven computer learning model. Instead of using complex algorithms that manually extract features and require specific lighting conditions, the system uses a trained neural network that can process raw image data directly, substituting mechanical processing with intelligent computation.
2Measurement precision
If traditional image-based methods are used, then measurement precision can be achieved, but productivity decreases due to human intervention and processing time
Solution Approach 1:
The computer learning model performs self-service by automatically processing image data and estimating pavement conditions without requiring human intervention. The model continuously processes new image data in real-time, eliminating the need for manual feature extraction, annotation, and analysis, thereby significantly improving productivity while maintaining precision.
Solution Approach 2:
The system enables continuous automated monitoring of pavement conditions through the computer learning model, which processes new image data as it is captured. This continuous action eliminates the intermittent nature of manual processing and allows for real-time condition assessment without interruption or human intervention.
3Measurement precision
If specific feature extraction and manual labeling are required, then measurement precision can be maintained, but device complexity and operational costs increase
Solution Approach 1:
The patent applies preliminary action by pre-training the computer learning model on a comprehensive dataset of labeled pavement condition images. This preliminary training phase allows the model to learn the complex mappings between image features and pavement conditions once, eliminating the need for manual feature extraction and labeling during actual operation. The model is ready to process new data immediately without requiring complex setup procedures.
Data Source
AI summary
Systems and methods are disclosed to label data collected by one instrument based on data collected by another instrument, train a computer learning model with the labeled data, and then use the trained computer learning model to estimate a condition from new unlabeled data. For example, weather-related pavement conditions may be estimated from camera images according to such systems and methods. Systems and methods are also disclosed to estimate road weather safety or hazard conditions using two different types of pavement conditions, such as road state and friction or grip coefficient, estimated from unlabeled camera images.