Abstract: Aiming at the existing flame detection algorithms having problems of low average detection accuracy and high missed detection rate of small target flames， an improved YOLOV5 flame detection algorithm is proposed. The algorithm uses the Transformer Encode module to replace the CSP bottleneck module at the end of the YOLOV5 backbone network， which enhances the network's ability to capture different local information and improves the average accuracy of flame detection. In addition， the CBAM attention module is added to the YOLOV5 networker， which enhances the network's ability to extract image features， and can better extract features for small target flames， reducing the missed detection rate of small target flames. Experiment with the algorithm on the public datasets BoWFire and Bilkent， the experimental results show that the average flame detection accuracy of the improved YOLOV5 network is higher， reaching 83.9%， the small target flame missed detection rate is lower， only 1.6%， and the detection rate is 34 frames/s. Compared with the original YOLOV5 network， the average accuracy is improved 2.4 percentage points， the small target flame missed detection rate is reduced by 4.1 percentage points， the improved YOLOV5 network can meet the real-time and precision requirements of flame detection.

Key words: YOLOV5 algorithm, Transformer, CBAM attention