A Real-Time Detection Algorithm of Flame Target Image

In many research tasks, the speed and accuracy of flame detection using supply chain have always been a challenging task for many researchers, especially for flame detection of small objects in supply chain. In view of this, we propose a new real-time target detection algorithm. The first step is to enhance the flame recognition of small objects by strengthening the feature extraction ability of multi-scale fusion. The second step is to introduce the K-means clustering method into the prior bounding box of the algorithm to improve the accuracy of the algorithm. The third step is to use the flame characteristics in YOLO+ algorithm to reject the wrong detection results and increase the detection effect of the algorithm. Compared with the YOLO series algorithms, the accuracy of YOLO+ algorithm is 99.5%, the omission rate is 1.3%, and the detection speed is 72 frames/SEC. It has good performance and is suitable for flame detection tasks.