激光与光电子学进展, 2020, 57 (4): 041010, 网络出版: 2020-02-20
基于迁移学习的卷积神经网络森林火灾检测方法 下载: 1640次
Forest Fire Detection Method Based on Transfer Learning of Convolutional Neural Network
图像处理 森林火灾检测 迁移学习 卷积神经网络 目标检测 image processing forest fire detection transfer learning convolutional neural network target detection
摘要
传统的卷积神经网络目标检测算法需要使用大量的数据来对网络参数进行训练,会花费大量的时间,并且森林火灾数据属于小样本数据。基于此,提出一种基于迁移学习的卷积神经网络森林火灾检测算法,该算法采用迁移学习的方法训练森林火灾检测网络模型。在建立的森林火灾数据集上进行实验,结果表明使用该算法进行森林火灾检测,准确率可达97%,具有准确率高、误报率低、检测时间短等优点,将其应用在森林火灾检测上具有一定的可行性。
Abstract
It takes significant time and a large amount of data for a traditional convolutional neural network-based target-detection algorithm to train its network parameters. Considering that forest fire data are small samples, this work investigates and implements a forest fire detection algorithm using the transfer learning method to train a convolutional neural network. Experiments on the forest fire dataset in this work show that the detection accuracy of this algorithm can reach 97%. In addition, the algorithm is more adaptable for forest fire detection as it has the advantages of high accuracy, low false alarm rate, and short detection time.
富雅捷, 张宏立. 基于迁移学习的卷积神经网络森林火灾检测方法[J]. 激光与光电子学进展, 2020, 57(4): 041010. Yajie Fu, Hongli Zhang. Forest Fire Detection Method Based on Transfer Learning of Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041010.