Smoke Detection in Storage Yard Based on Parallel Deep Residual Network

Zhenglai Wang; Min Huang; Qibing Zhu; Sheng Jiang

doi:10.3788/LOP55.051008

[1] Huang Z H. Brief discussion unit fire safety training[J]. Marine Fire, 37, 15-17(2016).

[2] Jiang M X, Wang H Y, Cai X Y. Early smoke detection based on codebook model and multiple features[J]. Journal of Image and Graphics, 17, 1102-1108(2012).

[3] Yu C Y, Fang J, Wang J J et al. Video fire smoke detection using motion and color features[J]. Fire Technology, 46, 651-663(2010). http://link.springer.com/article/10.1007/s10694-009-0110-z

[4] Yuan F N. Video-based smoke detection with histogram sequence of LBP and LBPV pyramids[J]. Fire Safety Journal, 46, 132-139(2011). http://www.sciencedirect.com/science/article/pii/S0379711211000026

[5] Ho C C. Machine vision-based real-time early flame and smoke detection[J]. Measurement Science and Technology, 20, 045502(2009). http://www.emeraldinsight.com/servlet/linkout?suffix=b6&dbid=16&doi=10.1108%2FEC-11-2012-0256&key=10.1088%2F0957-0233%2F20%2F4%2F045502

[6] Luo S, Jiang Y Z. State-of-art of video based smoke detection algorithms[J]. Journal of Image and Graphics, 18, 1225-1236(2013).

[7] Liu F, Liu P Y, Li B et al. Deep learning model design of video target tracking based on TensorFlow platform[J]. Laser & Optoelectronics Progress, 54, 091501(2017).

[8] Ye G L, Sun S Y, Gao K J et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 54, 081003(2017).

[9] Krstinic D, Stipanicev D, Jakovcevic T. Histogram-based smoke segmentation in forest fire detection system[J]. Information Technology and Control, 38, 237-244(2009). http://www.researchgate.net/publication/228968521_Histogram-Based_Smoke_Segmentation_in_Forest_Fire_Detection_System?ev=prf_cit

[10] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 5, 157-166(1994). http://jigpal.oxfordjournals.org/external-ref?access_num=10.1109/72.279181&link_type=DOI

[11] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 11, 249-256(2010). http://www.researchgate.net/publication/215616968_Understanding_the_difficulty_of_training_deep_feedforward_neural_networks

[12] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]. 2015 IEEE International Conference on Computer Vision (ICCV), 1026-1034(2015).

[13] LeCun Y A, Bottou L, Orr G B et al. . Efficient backprop[J]. Neural Networks: Tricks of the Trade, 9-48(2012).

[14] He K M, Sun J. Convolutional neural networks at constrained time cost[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5353-5360(2015).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778(2016).