A YOLOv7 Forest Fire Detection System with Edge Computing

Forest fires may cause serious human and economic losses, and it is also a key technology linked to realizing forest intelligent fire detection and prevention. Therefore, the study of forest fire detection and early warning system has great social application value. However, the traditional methods of detecting fires have weak real-time and detection capabilities. The new generation of deep learning technology, especially convolutional neural networks, provides new means and methods for fire detection. The convolutional neural network has the characteristics of good fault tolerance, self-adaptability, self-learning ability, and weight sharing, which makes the convolutional neural network can identify fires in a working principle close to that of the human eye. The fire detection method based on deep learning has high accuracy and robustness. However, due to its large number of parameters and a large number of calculations, it is difficult to be practically applied. With the development of edge computing, it is now possible to use the edge instead of the cloud for computing, which will greatly reduce latency. This paper collects a large number of forest fire images as a data set, trains a model that can recognize forest fires based on the YOLOv7 network, and further deploys the trained model to the edge server RK3588. Finally, a front-end visual interface capable of displaying fire recognition results was built using Pyqt5. The model in this method has good robustness and generalization ability, and the application of edge computing in fire forest detection further improves the speed of fire detection, which provides a new way for forest fire detection.