Automated Tire Wear Detection via Image Processing
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Solution Overview
Problem
Existing methods for determining tire surface wear are labor-intensive, time-consuming, and often result in inaccurate measurements, relying on manual processes or super-positioning techniques that require significant processing steps.
Innovation Solution
A system utilizing a camera-based input device that captures images or videos, which are then processed using morphology algorithms to automate the determination of tire wear patterns, including tread depth and width, by comparing histogram data with manufacturer specifications, providing accurate and efficient surface wear assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes or super-positioning techniques are used to determine tire wear, then measurement capability is achieved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical measurement processes with an automated image processing system. A camera captures images of the tire tread, and computer algorithms automatically analyze the images to determine wear patterns and depth, eliminating the need for manual insertion of gauges or visual inspection by human operators.
Solution Approach 2:
The system creates a digital copy of the tire tread surface through image capture. By processing this digital representation rather than physically measuring the actual tire, the system achieves rapid wear assessment without physical contact or manual intervention, significantly reducing time and labor requirements.
2Measurement precision
If manual inspection methods are used, then wear assessment is possible, but measurement accuracy deteriorates
Solution Approach 1:
The system replaces imprecise manual measurement with automated digital image analysis. The computer-based processing consistently applies measurement algorithms to the captured images, eliminating human error and subjectivity in wear assessment, thereby improving measurement precision while maintaining ease of operation through automated workflows.
3Difficulty of detecting and measuring
If super-positioning techniques are employed to determine wear patterns, then wear detection capability is achieved, but processing complexity increases
Solution Approach 1:
The system extracts the essential wear information directly from the tire tread image through automated analysis. By focusing on key features such as tread depth variations and wear patterns in the captured image, the system determines wear without requiring complex super-positioning techniques or multiple processing steps, thereby reducing overall system complexity.
4Productivity
If automated image processing is implemented, then productivity and accuracy improve, but computational requirements increase
Solution Approach 1:
The system applies image processing algorithms selectively to the most relevant regions of the tire tread image. By focusing computational resources on areas showing wear patterns or depth variations rather than processing the entire image uniformly, the system achieves accurate wear determination with reduced computational energy consumption while maintaining high productivity.
Data Source
AI summary
Implementations of the present disclosure provide a method and system for surface wear determination. According to one implementation, an image of an object surface is captured via an input device. A surface pattern is detected from the captured image and object data associated with the object surface is identified based on the detected pattern. Additionally, a surface wear value of the object surface is determined based on the object data and surface pattern.