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Fire Smoke Detection Based on Vision Transformer

When a fire occurs, it is first accompanied by heavy smoke features. In order to meet the real-time and accuracy requirements of fire smoke detection, a fire smoke detection technology based on deep learning and HSV color features is proposed. Firstly, the suspicious smoke range is roughly detected according to the color feature of the video image, and secondly, the accurate smoke detection is performed by the Vision Transformer algorithm to solve the overfitting problem and achieve the detection accuracy. Experiments show that the technology has high real-time performance, fast and accurate detection speed, and an accuracy rate of 99.05%, which meets the requirements of fire detection.