An outdoor fire recognition algorithm for small unbalanced samples

Abstract

Deep learning (DL) is a hot topic in the machine vision. A large number of data sets are necessary for efficient image recognition. Otherwise the overfitting will easily occur. However, most actual samples are limited and unbalanced. To diminish the negative impact of small unbalanced samples on image recognition, the Deep Convolutional Generative Adversarial Network (DC-GAN) was improved to simulate data distribution, and relied on the improved network to generate a highly diverse dataset of balanced fire images in the work. Then, the number of output layer nodes was finetuned for the training of the target dataset by the layer freezing method. The training of small unbalanced samples was realized, using exponentially decaying learning rate, L2 regularization method, and Adam optimization algorithm. Simulation results showed that the proposed algorithm converged faster by fixing the convolutional layer parameters of the pre-trained model and finetuning the fully connected layer through transfer learning. Besides, 99% of fire images were correctly recognized, without inducing the problem of small sample overfitting. The proposed algorithm provides a desirable tool for outdoor fire recognition.

Keywords

• Small unbalanced samples
• Convolutional neural network (CNN)
• Fire image recognition
• Generative Adversarial Network
• Transfer learning