A Survey on Tire Tread Depth Calculation Using Mobile Camera

Authors:

Mr.Abhilash L Bhat1, Prajwal N2, Srikanth P V3, Niveditha Nag N V S4, R Nitish5 Assistant Professor, Dept of CSE, KSIT, Karnataka, India1

Student, Dept of CSE, KSIT, Karnataka, India2-5

ABSTRACT

Vehicle safety is highly dependent on tire condition, particularly tire tread depth, which directly affects traction, braking efficiency, and vehicle stability on wet or uneven roads. Worn-out tire treads increase the risk of skidding and accidents, making regular tire inspection essential. Traditional tread measurement methods such as manual gauges and professional inspections are time-consuming, inconvenient, and often ignored by regular users. This creates the need for an automated, accessible, and cost-effective tire monitoring solution.

This paper presents a mobile camera–based tire tread depth calculation system using image processing and computer vision techniques. The proposed framework uses smartphone cameras to capture tire images and applies preprocessing operations such as grayscale conversion, noise reduction, and edge detection to identify tread patterns and grooves. By analyzing image features and geometric measurements, the system estimates tire tread depth and compares it with standard safety thresholds to determine tire condition.

The system integrates OpenCV-based image processing with a Python backend to provide automated and real-time tire safety analysis. Experimental results on tire image datasets demonstrate reliable tread detection accuracy under different lighting and environmental conditions. The proposed solution offers a simple, low-cost, and non-contact approach for vehicle safety monitoring, automobile maintenance, fleet management, and smart vehicle diagnostics.

By transforming manual tire inspection into an intelligent smartphone-based monitoring system, the proposed framework contributes toward improved road safety, preventive vehicle maintenance, and accessible tire health evaluation for everyday users.

Keywords- Tire tread depth, image processing, computer vision, mobile camera, OpenCV, edge detection, tread pattern analysis, tire wear detection, smartphone-based monitoring, vehicle safety, automated tire inspection, real- time analysis, feature extraction, smart vehicle diagnostics, road safety monitoring.