Open AccessCompressed dual‐channel neural network with application to image‐based smoke detection
Author(s) -
Zhang Jiedong,
Xie Wenhui,
Liu Hongyan,
Dang Wenyi,
Yu Anfeng,
Liu Di
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12205
Subject(s) - convolution (computer science) , convolutional neural network , computer science , smoke , superposition principle , channel (broadcasting) , separable space , process (computing) , artificial intelligence , deep learning , image (mathematics) , algorithm , pattern recognition (psychology) , artificial neural network , mathematics , telecommunications , engineering , mathematical analysis , operating system , waste management
Abstract Effective detection of smoke from visual scenes can play a vital role not only in industrial safety as an early warning system but also in forest fire prevention. However, it is difficult to detect smoke based on texture and color. Therefore, many researches have been conducted on this issue and derived detection methods based on convolutional neural networks (such as DNCNN and DCNN etc.). However, in the process of convolution, with the superposition of convolutions times, the parameters of the network increase gradually and thus cause a large computational burden, which brings about the problem of unsatisfactory operating efficiency. Thus, this paper mainly introduces the depthwise separable convolution into the state‐of‐the‐art DCNN developed specifically for smoke detection, dubbed as the improved DCNN (IDCNN). Compared with standard convolution, by introducing the depthwise separable convolution, the convolution parameters and the corresponding calculation amount in the process of convolution can be greatly reduced, so that the network can deal with more data in a shorter time which improves operating efficiency. Experimental results demonstrate the effectiveness of IDCNN as compared with the state‐of‐the‐art deep networks for smoke detection based on standard convolution in terms of parameter quantity and running speed.