Anomaly monitoring and early warning of electric moped charging device with infrared image

Abstract

Potential high-temperature risks exist in heat-prone components of electric moped charging devices, such as sockets, interfaces, and controllers. Traditional detection methods have limitations in terms of real-time performance and monitoring scope. To address this, a temperature detection method based on infrared image processing has been proposed: utilizing the median filtering algorithm to denoise the original infrared image, then applying an image segmentation algorithm to divide the image. After trimming the image boundaries, the corner detection algorithm is used to extract corner information from the image, acquiring the position of the rectangular selection area that includes all corners. Finally, the block partitioning algorithm is used for temperature detection of the selected area and to determine the type of fault in the charging device based on the threshold. Based on this method, an electric moped charging device monitoring and warning system using infrared images has been designed. Experimental results demonstrate that this monitoring system can effectively detect the location and temperature of the test area, display and transmit warning information in real time via software, and has a high accuracy rate. This system holds promise as a crucial tool for enhancing the safety of electric moped charging devices.

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