目前，市場上的輪胎檢測系統，多數是以量測胎壓或胎溫來判斷輪胎是否異常，並無法有效預測輪胎壽命及辨識老化程度、異常狀況的輪胎檢測產品如果正常駕駛對於自身輪胎的狀況有疏忽，可能將造成難以想像的意外。因此本計畫研究一套基於影像辨識技術之輪胎異常檢測系統，透過影像辨識、機器學習、AI演算法來預測輪胎壽命及老化程度，辨別輪胎花紋的老化程度和異常狀況，並結合所本計畫自己開發的智慧型手機APP和網頁管理平台來達成如：1.即拍即檢測快速檢測自身輪胎狀況，2.具輪胎檢測、輪胎使用紀錄、預估里程數、用戶意見回饋等功能之手機應用程式3.基於深度學習卷積神經網路技術辨識各式輪胎異常狀況4.建立跨平台輪胎管理網頁平台介面供車廠或一般使用者，等多項功能，經實驗測試結果，本系統之AI辨識準確度高達93.99%。

Currently, most tire inspection systems on the market rely on measuring tire pressure or temperature to determine whether the tires are abnormal. However, these systems cannot effectively predict tire lifespan, identify aging levels, or detect abnormal tire conditions. Neglecting the condition of one’s own tires during normal driving may lead to unforeseen accidents. Therefore, this project aims to develop a tire anomaly detection system based on image recognition technology. The proposed system utilizes image recognition, machine learning, and AI algorithms to predict tire lifespan and aging levels. It distinguishes between the aging levels and abnormal conditions of tire treads. Additionally, the project includes the development of a smartphone app and a web management platform. The objectives include: 1. Instantaneous inspection through on-the-spot photo capture, allowing for a quick assessment of tire condition. 2. A mobile application featuring tire inspection, tire usage records, estimated mileage, user feedback, and other functionalities. 3. Utilization of deep learning convolutional neural network technology for recognizing various tire anomaly conditions. 4. Establishment of a cross-platform tire management web interface for use by automotive manufacturers and general users.

Through experimental testing, the system demonstrates an impressive AI recognition accuracy of up to 93%.