Tire-Wear Estimation System Using Semantic Segmentation and Its Deployment

Abstract

Tire-wear significantly impacts vehicle operation and passenger safety, making tire-wear monitoring a critical task. Traditional manual groove-depth measurements are precise but impractical for general drivers. Existing sensor and intelligent tire-based methods also have limitations that require additional equipment. This paper presents a novel Tire-Wear Estimation (TWE) system using mobile phone cameras, which leverages close-up tire videos to estimate individual groove-depths. Due to the lack of an existing dataset that captures tire grooves, we have collected a large number of tire videos with the ground truths of groove-depths to build and evaluate our system. For the semantic segmentation model, we select one from the U-Net family by considering complexity as well as output quality, and develop a post-processing method to improve the quality of masks (segmentation results). After obtaining frame-wise tire-masks from the input video, we measure the width and depth of each dent (the indented parts in the masks). By tracking the dimensions of dents over the video frames, we estimate the actual depth of grooves. Additionally, we implement a model lifecycle-based service to improve the performance of our TWE system. Since it is not feasible to inspect all user inputs and their results, we have also developed a mask quality pre-screening method based on mask generation to facilitate the data validation process. The proposed TWE system has shown an absolute error of 0.94 mm , with an average latency of 2.44 seconds, to obtain results from tire videos of around 10 seconds.