Video smoke detection with domain knowledge and transfer learning from deep convolutional neural networks

• DOI: 10.1016/j.ijleo.2021.166947
• Publication Date: 2021-04-19T22:53:47Z
• Authors: Yang Jia; Weiguang Chen; Manjiang Yang; Liangwu Wang; Dongcai Liu; Qixing Zhang
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract Video smoke detection (VSD) is a prospective and effective solution for fire detection in spacious buildings and forests. Most of the deep learning based VSD model are end-to-end models, and the intrinsical knowledge of the smoke, such as the motion and color which are obvious and effective for detection have not been utilized effectively. In order to improve the detection rate and reduce the false positive rate of the VSD systems, domain knowledge of smoke was used to segment suspected smoke regions in a video frame firstly in this detection framework. Then a deep neural network was designed to extract the features of smoke regions and distinguish smoke regions from all suspected region. For the intrinsical source domain and target domain, source task and target task, transfer learning of Alex-net, Inception V3 and ResNet pre-trained had been used to distinguish smoke regions in this work. A dataset composed of smoke images and other common objects was established to train the smoke recognition model. The F-beta value can be as high as 0.99. Experiments show that domain knowledge is important for smoke detection in the preliminary stage. Meanwhile, instance-transfer learning and parameter-transfer learning can be potentially effective solutions for VSD.