关键词: 深度学习, 林火, 图像识别, 目标检测, SSD算法

Abstract:

【Objective】 Forest fires not only cause serious damage to the natural environment of forests and affect their ecosystem functions, but also cause harm and losses to human society. Based on the use of a deep learning process to identify forest fire images, it is possible to efficiently and accurately identify images showing the early stages of forest fires, thereby enabling prompt action to reduce the harm caused by forest fires to forest ecosystems and human society. 【Method】 With the implementation of the single shot multibox detector (SSD) target detection algorithm based on the TensorFlow framework, the network structure was optimized according to the characteristics of forest fires, and a model was developed that could identify flame features in forest fire images. First, the acquired images were normalized and then the “Imgaug” image data enhancement library was used to perform data enhancement on forest fire images and build a forest fire dataset, followed by the building of a deep learning operating environment and the setting of hyper-parameters. The recognition of forest fire features in the image by the model was obtained by testing the data in the test set. By visualizing both the Loss curve and the precision-recall (P-R) curve, the recognition accuracy of the model for forest fires was ultimately obtained.【Result】 With an increase in the number of iterations, the loss value gradually decreased from 35.31 in the early stage of training, and stabilized at about 7.10 when the training reached 20 000 iterations. At this point, the recognition accuracy of the model reached a high level. The recognition confidence of fire features was greater than 0.9. A test set was built based on the forest fire images in the fire luminosity airborne-based machine learning evaluation (FLAME) public data set. After testing and evaluation, the model achieved a mean average precision for forest fire characteristics of 97.40%, the missed detection rate was 0.03, and the average detection time of the test image was only 0.07 s. Compared with the faster- region-based convolutional neural network (Faster R-CNN) model using the same data set, the SSD achieved a better detection speed and recognition accuracy. 【Conclusion】 The SSD algorithm for forest fire feature recognition proposed here can take into account the detection speed and precision, and can quickly identify images in the early stage of a forest fire with a low missed detection rate, which will enable forest staff to deal with the fire in time, providing a technical reference for the early prevention of forest fires.

Key words: deep learning, forest fire, image identification, object detection, single shot multibox detector (SSD) algorithm