Abstract
The braking system of a vehicle is the most crucial component for ensuring the safety on the road. Moreover, the extraction of temperature field data from a vehicle's tires plays a crucial role in vehicle condition monitoring. Nevertheless, the conventional infrared thermal imaging detection method necessitates manual recognition of the tire components and marking of each temperature point one by one in the infrared image. To address this issue, a method combination with dual-light fusion and deep learning is proposed to implement automatic monitoring for vehicle brake drum temperature. Firstly, we propose a late-end multimodal fusion algorithm based on the YOLOv5 network to integrate visible light and infrared thermal imaging This algorithm allows for feature extraction from both modalities and performing decision-level image fusion, thereby improving the robustness of automatic vehicle and tire detection. Additionally, we employ a semantic segmentation network to accurately segment the tires, extract the relevant visible pixel information, and obtain the temperature field matrix from the infrared images while eliminating extraneous background details. Subsequently, a three-dimensional model of tire’s temperature field is constructed obtaining the dynamics of tire temperature during the vehicle operation. Through experimentation, we demonstrate the efficacy of our proposed method in achieving all-weather roadside vehicle classification and tire temperature monitoring, which provides technical and theoretical support for the active risk prevention and control in road transportation.
Published Version
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