Jie Zhang , Jiaqiang Peng , Xuan Kong , Lu Deng , Eugene J. OBrien
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Vision-based identification of tire inflation pressure using Tire-YOLO and deflection
Tire inflation pressure has a significant impact on vehicle performance and safety. Existing tire pressure identification methods rely on sensors, which have limitations such as high maintenance costs and poor durability. This study proposes a non-contact method for tire pressure identification using Tire-YOLO and deflection. Firstly, a Tire-YOLO model is developed to identify tire specifications in images, which are used to obtain recommended pressure referring to the standards. Then, the statistical relationship between actual and recommended pressures is established to obtain statistical pressure. Next, the statistical pressure is corrected using the visually identified tire deflection, serving as an approximation of actual pressure. Finally, field tests are conducted on different tire types to verify the proposed method. The results indicate that the inflation pressures determined using this method are within 10% of actual pressures for all the cases. The proposed method provides a potential way for rapid identification of inflation pressure.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.