Open AccessJoint Network Smoke Recognition based on Channel Attention Mechanism
Author(s) -
Shanju Jin,
Tongzhou Zhao,
Xiaoyun An
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/4/042061
Subject(s) - smoke , mechanism (biology) , computer science , joint (building) , channel (broadcasting) , feature (linguistics) , artificial intelligence , artificial neural network , pattern recognition (psychology) , computer network , engineering , physics , architectural engineering , linguistics , philosophy , waste management , quantum mechanics
Aiming at the problems that traditional fire smoke recognition methods in a low recognition accuracy, a fusion network based on VGG16 is proposed, which use channel attention mechanism and contain Dense Blocks network to extract smoke features. To avoid the loss of smoke features, channel attention mechanism in backbone network is automatically to learn the importance of feature in this network. The experiment results show that the accuracy of this network is 3.0% higher than VGG16 neural network, and which is effective and feasible in smoke recognition tasks.