激光与光电子学进展, 2018, 55 (5): 051008, 网络出版: 2018-09-11
基于并行深度残差网络的堆场烟雾检测方法 下载: 1308次
Smoke Detection in Storage Yard Based on Parallel Deep Residual Network
图像处理 图像识别 堆场 烟雾检测 并行深度残差网络 image processing image recognition storage yard smoke detection parallel deep residual network
摘要
堆场烟雾检测对于火灾预警、保障人员与财产安全具有重要意义。针对传统烟雾检测方法特征提取不充分,误报率偏高以及稳健性较差的问题,提出一种基于并行深度残差网络的堆场烟雾检测方法。该方法利用目标场景烟雾RGB图像的R 、G 、B 分量以及图像HSI变换的H 、S 、I 分量构建并行深度残差网络,自适应获得烟雾特征;同时通过样本扩边、负样本强化学习策略来加强模型对类烟物体的判别能力。实验结果表明,该算法能有效降低因类烟物体产生的误报率,且提升了网络的检出率和稳健性。
Abstract
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.
王正来, 黄敏, 朱启兵, 蒋胜. 基于并行深度残差网络的堆场烟雾检测方法[J]. 激光与光电子学进展, 2018, 55(5): 051008. 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.