The core link in the construction of smart ecological forestry is the monitoring and prevention of forest fires. In order to put out the fire source to prevent the spread of fire and eliminate the hidden danger of forest fires in the first time, two forest fire detection models, YOLO_MC and YOLO_MCLite, are proposed for UAV aerial inspection. Among them, YOLO_MC can detect open flame and smoke in standard images. Based on the lightweight of YOLO_MC model, a detection model YOLO_MCLite suitable for high temperature region in thermal images is proposed. In the design of the network structure, the Transformer model is firstly integrated into the conventional convolutional neural network, which improves the perception ability of the backbone network for global feature information; at the same time, lightweight design of the Transformer model is performed. First, reduce the number of tokens in the form of group computation on the network structure to reduce the amount of tokens. Second, deredundant and weighted the number of channels in feature blocks through channel attention mechanism to reduce the dimension parameters of tokens to reduce the computational complexity. And the distillation algorithm is also used to extract the ultralightweight network from the designed network and apply it to the detection of forest high temperature points in the infrared image of the UAV to prevent the occurrence of forest fires. After experiments, the following data are obtained: the detection accuracy of the designed detection model for open flame and smoke can reach 948%, and the detection accuracy for high temperature points can reach 972%. And the test frame rate on the NVIDIA JETSON TX2 embedded device reached 225 and 324 respectively. The experimental results show that the network designed in this paper can effectively detect forest fires and prevent fires in time by detecting high temperature points.