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In order to solve the problem that the existing fire detection algorithm model is complex, the real-time performance is poor, and it is difficult to deploy on the UAV platform, the UAV fire image target detection is analyzed and studied by improving yolov5s algorithm. Use the fire image, public data set and Internet aerial video obtained by the UAV equipment equipped with high-definition camera to independently establish the UAV fire image data set; The lightweight model yolov5s is used as the basic model and mobilenetv3 is used as the feature extraction backbone network to reduce the model parameters and computation, and solve the problems of poor real-time performance and model deployment; The attention module CBAM is introduced into the neck of the model, which integrates channel and spatial information to strengthen the transmission of high-level semantic information; Modify the head structure of the model to enhance the ability of small target detection. Through ablation test, the influence of each module on the model is compared and analyzed with common fire models, and the advantages and disadvantages of this algorithm are analyzed. The Average accuracy of the algorithm on the self built data is 78.2%, the model size is 6.7m, and the single frame is 640 × 640) the image processing time is 15ms. The experimental results show that the algorithm model in this paper is simple and has good real-time performance, which lays a technical foundation for the deployment of fire detection algorithm in UAV platform.