UAV forest fire detection based on lightweight YOLOv5 model

In recent years, the frequent occurrence of forest fires has caused serious impact on the environment and economy. Fire detection has become a hot research direction. Despite the remarkable achievements, the unmanned aerial vehicle (UAV) still has some problems such as insufficient precision and excessive parameters. In order to improve the application ability of UAV in forest fire prevention and control, a lightweight target detection model based on YOLOv5 is proposed. The model is based on the overall structure of YOLOv5, MobileNetV3 is used as the backbone network, and semi-supervised knowledge distillation (SSLD) is used for training to improve the convergence speed and accuracy of the model. The final model size was reduced by 94.1% from 107.6 MB to 6.3 MB. mAP0.5 increased by 0.8% and mAP0.95 increased by 2.6%. The improved lightweight YOLOv5 model has fewer parameters and less computation, which confirms that MobileNetV3 has an excellent effect on the compression of model memory, and the semi-supervised knowledge distillation method is beneficial to improve the accuracy of the model. In the future, the accuracy of the model and the detection rate of the covered flame should be further improved.