Tire Tread Image Estimation for Vehicle-Independent Groove Depth
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Solution Overview
Problem
Existing tire wear estimation devices cannot estimate the depth of a tire groove without vehicle-specific information, limiting their effectiveness.
Innovation Solution
An estimation device that acquires an image of the tire tread and estimates the groove depth using a processor, which also considers travel history and actual measurements to improve accuracy and trigger re-learning of the depth estimation model when discrepancies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle-specific information is required for tire groove depth estimation, then estimation accuracy may be improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts the essential estimation functionality from a complex vehicle-specific system to a simplified image-based system. By focusing on extracting groove depth information directly from tread images rather than requiring comprehensive vehicle data, the system achieves acceptable estimation accuracy with reduced complexity.
Solution Approach 2:
The estimation device is designed to handle multiple tire types and vehicle conditions using a universal image processing approach. The system can estimate groove depth for various tire patterns and vehicle types without requiring vehicle-specific calibration data, making the device universally applicable.
2Ease of operation
If a simple image-based estimation method is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where estimation results are continuously refined. By comparing estimated groove depths with actual measurement data when available, the system adjusts and improves its estimation accuracy over time while maintaining operational simplicity.
Solution Approach 2:
The system performs preliminary estimations using image data that can be immediately processed, providing quick results. When actual measurements are taken, this preliminary estimation serves as a baseline that can be refined, ensuring both rapid operation and improved precision.
3Measurement precision
If the estimation model is frequently re-learned with actual measurements, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system implements periodic re-learning rather than continuous re-learning. The estimation model is updated at scheduled intervals or when triggered by specific conditions (such as accumulating sufficient actual measurement data), balancing precision improvement with time efficiency.
Solution Approach 2:
The system performs partial re-learning by selectively updating the model only when necessary, rather than continuously re-training. This approach uses actual measurement data to make targeted improvements to the estimation model without the time cost of complete re-learning cycles.
Data Source
AI summary
An estimation device includes a memory; and a processor coupled to the memory, wherein the processor is configured to: acquire an image of a tread of a tire that is installed at a vehicle; and estimate a depth of a groove of the tire based on the image.