The Tires Worn Monitoring Prototype System Using Image Clustering Technology

Abstract

In order to improve traffic safety, researchers studied the driver’s physical or mental monitoring, vehicle structure, airbags, brake systems and tires and so on for continuous improvement. The tires need to carry the vehicle loading, to enhance grip, to improve the drainage ability and to reduce the friction noise. Accordingly, tire wear will affect the aforementioned features. This study had designed an experiment platform which can detect the main tread depth, applying image clustering technique, under conditions of low tire speed. In addition, the proposed image clustering algorithm FCM_sobel, could measure the depth of the main tread, at α = 0.5 (the influence weighting of the neighboring pixels) and rotating cycle equal 2.5 seconds/rotation. The implemental results show that the precision rates were 93.41%, 96.86 % for the depths of the main tread Iand II respectively. Consequently, detected the depth of the main tread I, the precision rate improved 3% compared with FCM_S1.