Laser & Optoelectronics Progress, Volume. 55, Issue 5, 051008(2018)
Smoke Detection in Storage Yard Based on Parallel Deep Residual Network
Smoke detection in storage yard has great signification for fire early warning and protecting the safety of personnel and property. To solve the problem of insufficient features extraction, high false positive rates and poor robustness of traditional smoke detection methods, a new method of smoke detection in storage yard based on the parallel deep residual network is proposed. This method builds the parallel deep residual network with R, G, B components of the smoke RGB image and H, S, I components of the HSI transform image to adaptively extract the features. Meanwhile, the discriminant ability for the target like-smoke of the model is enhanced by the strategy including expanding the sample scale and reinforcement learning of the negative samples. The experimental results show that the proposed algorithm can effectively reduce the false positive rate caused by target like smoke and improve the detection rate and robustness of network.
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Zhenglai Wang, Min Huang, Qibing Zhu, Sheng Jiang. Smoke Detection in Storage Yard Based on Parallel Deep Residual Network[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051008
Category: Image processing
Received: Oct. 17, 2017
Accepted: --
Published Online: Sep. 11, 2018
The Author Email: Huang Min (huangmzqb@163.com)