DB-Net: Dual Attention Network with Bilinear Pooling for Fire-Smoke Image Classification

Abstract

In recent years, with the help of deep learning, image classification has been significantly improved. More and more researchers are seeking fire alarm solutions based on these artificial intelligence algorithms. However, this raises new challenges in an effective method of classification for fire-smoke images. In this paper, we propose a Dual Attention Network with Bilinear Pooling of three steps to accomplish the task of fire-smoke classification. We first extract features of fire pictures or smoke ones by using ResNet-50 as a basic net. Then we attach two kinds of attention modules, channel attention and spatial attention, which are called dual attention, to extract key information ‘what’ and ‘where’ from pictures. Finally, we merge them by using the bilinear pooling module which has been shown to be effective at improving our classification rate. Results show that our most accurate model can reach 90.11% per-image accuracy, which is improved by 4.81% compared to the traditional ResNet-50.