Portable Device for Faster Tire Tread Depth Estimation Using Linear CCD Sensors

• Derek N. A. Alves Edge Innovation Center / Vertex Institute of Innovation and Technology / UFAL
• André dos Santos Edge Innovation Center / Vertex Institute of Innovation and Technology
• Bruno G. Ferreira Edge Innovation Center / Vertex Institute of Innovation and Technology / University of Porto
• Tiago F. Vieira Edge Innovation Center / Vertex Institute of Innovation and Technology / UFAL

Resumo

To improve safety and efficiency of land vehicles, such as cars and trucks, frequent tire inspections are necessary. Regular checks of tire tread depths help prevent accidents and enhance fuel efficiency. However, traditional methods for estimating tread depth, like depth gauges, can be both time-consuming and imprecise, especially for vehicles with multiple tires. In this paper, we propose a novel portable embedded Digital Processing System (DPS) device that utilizes linear image sensors (CCDs) and active illumination to estimate the depths of all tire treads at once. The device features a microcontroller that configures the linear sensor's parameters and collects its readings. The microcontroller communicates with an Android device via a serial connection to transfer the data for processing. Additionally, the Android application offers visual feedback to the user, enabling them to conduct tire inspections on vehicles. Both the Printed Circuit Board (PCB) and the device case are designed in our laboratory, with the case being 3D printed. The proposed device achieves an error of 0.115 millimeters in tread depth estimation and consumes only 22.15% of the time required by the traditional depth gauge to estimate all the tread depths of a tire. The integration of the proposed device into tire manufacturing companies can significantly enhance the efficiency of tread inspection processes. This innovation not only improves overall quality control and productivity in tire inspection but also offers substantial benefits in both scientific and economic contexts.

Palavras-chave: Tire Tread Depth Estimation, Tire State Analysis, Embedded System, Tire Inspection, Linear Sensor, Light Enhancement, Android device