Multilevel feature cooperative alignment and fusion for unsupervised domain adaptation smoke detection

Abstract

Early smoke detection using Digital Image Processing technology is an important research field, which has great applications in reducing fire hazards and protecting the ecological environment. Due to the complex changes of color, shape and size of smoke with time, it is challenging to accurately recognize smoke from a given image. In addition, limited by domain shift, the trained detector is difficult to adapt to the smoke in real scenes, resulting in a sharp drop in detection performance. In order to solve this problem, an unsupervised domain adaptive smoke detection algorithm rely on Multilevel feature Cooperative Alignment and Fusion (MCAF) was proposed in this paper. Firstly, the cooperative domain alignment is performed on the features of different scales obtained by the feature extraction network to reduce the domain difference and enhance the generalization ability of the model. Secondly, multilevel feature fusion modules were embedded at different depths of the network to enhance the representation ability of small targets. The proposed method is evaluated on multiple datasets, and the results show the effectiveness of the method.