Tire Assistant is an application that utilizes deep learning models to analyze images of tires taken by users. It determines the appropriate tire dimensions for the relevant vehicle, identifies the tire brand, and provides web pages containing information about these tires.

Ease of Use and Solution-Focused

Tire replacement is a time-consuming and challenging process for drivers. Tire Assistant aims to simplify this process. Users can quickly and easily determine the size, brand, and model of their tires by taking a photo of them through this application. This enables users to access suitable tire options and purchase links with ease.

How doest the method work?

Tire Assistant incorporates an artificial intelligence model that analyzes the tire photos taken by the user. This process consists of several stages:

1. Firstly, the tire photo undergoes object detection modeling. This model detects the tire inscriptions in the photo.
2. Subsequently, these inscriptions are converted into text using Optical Character Recognition (OCR) technology. Optik Karakter Tanıma (OCR) teknolojisi ile yazıya dönüştürülür.
3. These texts are then fed into a Bidirectional Recurrent Neural Network (RNN) model. This model identifies the tire's size information and model.ki yönlü RNN modeline verilir. Bu model, lastiğin ebat bilgisi ve modelini tespit eder.

Project Result

Tire Assistant has a pipeline structure containing the algorithms listed above. The current version of this structure operates in the containerized environment of Azure web services.

At Golive-R&D, with our Tire Assistant project, we aim to simplify the tire selection process and save time for users. Thanks to this application, tire replacement has now become a much faster and easier procedure