A Dual-Channel convolution neural network for image smoke detection

Image smoke detection is a challenging task due to the difference of color, texture, and shape of smoke. In recent years, deep learning has greatly improved the performance of image classification and detection. In this paper, we propose a Dual-Channel Convolutional Neural Network (DC-CNN) using transfer learning for detecting smoke images. Specifically, an AlexNet network with transfer learning, used to extract generalized features, is designed on the first channel as the main framework of entire network. The second channel is a tidy convolution neural network for extracting specific and detailed features. To guarantee the robustness of the network, two channels of the network are trained separately and their features are fused in the concat layer. The experimental data sets consist of smoke images and non-smoke images, and some challenging non-smoke images are added into the data sets as a supplement. Experimental results show that the proposed method can work effectively and achieve detection rate above 99.33%.

[1]  ByoungChul Ko,et al.  Wildfire smoke detection using spatiotemporal bag-of-features of smoke , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[2]  ByoungChul Ko,et al.  Spatiotemporal bag-of-features for early wildfire smoke detection , 2013, Image Vis. Comput..

[3]  Qinghua Hu,et al.  Generalized Latent Multi-View Subspace Clustering , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Huimin Lu,et al.  Low illumination underwater light field images reconstruction using deep convolutional neural networks , 2018, Future Gener. Comput. Syst..

[5]  A. Enis Çetin,et al.  Wavelet based real-time smoke detection in video , 2005, 2005 13th European Signal Processing Conference.

[6]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[7]  Marimuthu Palaniswami,et al.  Smoke detection in video using wavelets and support vector machines , 2009 .

[8]  Giovanni De Magistris,et al.  Transfer Learning from Synthetic to Real Images Using Variational Autoencoders for Precise Position Detection , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[9]  Darko Stipanicev,et al.  Visual spatial-context based wildfire smoke sensor , 2012, Machine Vision and Applications.

[10]  Feiniu Yuan,et al.  A fast accumulative motion orientation model based on integral image for video smoke detection , 2008, Pattern Recognit. Lett..

[11]  Yu Zhou,et al.  Negative transfer detection in transductive transfer learning , 2017, International Journal of Machine Learning and Cybernetics.

[12]  Simone Calderara,et al.  Vision based smoke detection system using image energy and color information , 2011, Machine Vision and Applications.

[13]  Giovanni De Magistris,et al.  Transfer learning from synthetic to real images using variational autoencoders for robotic applications , 2017, ArXiv.

[14]  Thou-Ho Chen,et al.  An intelligent real-time fire-detection method based on video processing , 2003, IEEE 37th Annual 2003 International Carnahan Conference onSecurity Technology, 2003. Proceedings..

[15]  Xiaojun Wan,et al.  Co-Training for Cross-Lingual Sentiment Classification , 2009, ACL.

[16]  Yue Wang,et al.  Real-time smoke detection using texture and color features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[17]  Debjani Chakraborty,et al.  Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. , 2017, Biomedical optics express.

[18]  Zhitao Xiao,et al.  A Dual Convolution Network Using Dark Channel Prior for Image Smoke Classification , 2019, IEEE Access.

[19]  Wanqing Li,et al.  Smoke detection in videos using Non-Redundant Local Binary Pattern-based features , 2011, 2011 IEEE 13th International Workshop on Multimedia Signal Processing.

[20]  Bo Du,et al.  Unsupervised transfer learning for target detection from hyperspectral images , 2013, Neurocomputing.

[21]  Feiniu Yuan,et al.  A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection , 2012, Pattern Recognit..

[22]  Xiaojun Wan,et al.  Cross-Language Opinion Target Extraction in Review Texts , 2012, 2012 IEEE 12th International Conference on Data Mining.

[23]  Carlo S. Regazzoni,et al.  Early fire and smoke detection based on colour features and motion analysis , 2012, 2012 19th IEEE International Conference on Image Processing.

[24]  Feiniu Yuan,et al.  A Deep Normalization and Convolutional Neural Network for Image Smoke Detection , 2017, IEEE Access.

[25]  Michael Gamon,et al.  Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms , 2005, ACL 2005.

[26]  Huimin Lu,et al.  PEA: Parallel electrocardiogram-based authentication for smart healthcare systems , 2018, J. Netw. Comput. Appl..

[27]  A. Enis Çetin,et al.  Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video , 2012, IEEE Transactions on Image Processing.

[28]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[29]  Huimin Lu,et al.  Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning , 2018, IEEE Internet of Things Journal.

[30]  Lei Wang,et al.  Detection and Separation of Smoke From Single Image Frames , 2018, IEEE Transactions on Image Processing.