• Title/Summary/Keyword: Tread Wear Classification

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Classification of Tire Tread Wear Using Accelerometer Signals through an Artificial Neural Network (인공신경망을 이용한 가속도 센서 기반 타이어 트레드 마모도 판별 알고리즘)

  • Kim, Young-Jin;Kim, Hyeong-Jun;Han, Jun-Young;Lee, Suk
    • Journal of the Korean Society of Industry Convergence
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    • v.23 no.2_2
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    • pp.163-171
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    • 2020
  • The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

Efficient Tire Wear and Defect Detection Algorithm Based on Deep Learning (심층학습 기법을 활용한 효과적인 타이어 마모도 분류 및 손상 부위 검출 알고리즘)

  • Park, Hye-Jin;Lee, Young-Woon;Kim, Byung-Gyu
    • Journal of Korea Multimedia Society
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    • v.24 no.8
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    • pp.1026-1034
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    • 2021
  • Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.