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收稿：2018-06-20，
修回：2018-7-13，
纸质出版：2019-02-16
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目的
2
通过烟雾检测能够实现早期火灾预警，但烟雾的形状、色彩等属性对环境的变化敏感，使得烟雾特征容易缺乏辨别力与鲁棒性，最终导致图像烟雾识别、检测的误报率与错误率较高。为解决以上问题，提出一种基于Gabor滤波的层级结构，可视为Gabor网络。
方法
2
首先，构建一个Gabor卷积单元，包括基于Gabor的多尺度、多方向局部响应提取和跨通道响应浓缩；然后，将Gabor卷积单元输出的浓缩响应图进行跨通道编码并统计出直方图特征，以上Gabor卷积单元与编码层构成了一个Gabor基础层，用于提取多尺度、多方向的基础特征，对基础层引入最大响应索引编码和全局优化能生成扩展特征；最后，将基础和扩展特征首尾相连形成完整烟雾特征，通过堆叠上述Gabor基础层能形成一个前馈网络结构，将每一层特征首尾相连即可获得烟雾的多层级特征。
结果
2
实验结果表明，此Gabor网络泛化性能好，所提烟雾特征的辨别力在对比实验中综合排名第一，所提纹理特征的辨别力在两个纹理数据集上分别排名第一与第二。
结论
2
所提Gabor网络能够实现多尺度、多方向的多层级纹理特征表达，既能提高烟雾识别的综合效果，也可提高纹理分类的准确率。未来可进一步研究如何降低特征的冗余度，探索不同层特征之间的关系并加以利用，以期在视频烟雾实时识别中得到实际应用。
Objective
2
Smoke frequently occurs earlier than flames when fire breaks out. Thus
smoke detection provides earlier fire alarms than flame detection. The color
shape
and movement of smoke are susceptible to external environment. Thus
existing smoke features lack discriminative capability and robustness. These factors make image-based smoke recognition or detection a difficult task. To decrease the false alarm rates (FARs) and error rates (ERRs) of smoke recognition without dropping detection rates (DRs)
we propose a Gabor-based hierarchy (termed GaborNet) in this study.
Method
2
First
a Gabor convolutional unit
which consists of a set of learning-free convolutional kernels and condensing modules
is constructed. Gabor filters with fixed parameters generate a set of response maps from an original image as a multiscale and multi-orientation representation. In addition
a condensing module conducts max pooling across the channels of every response map to capture subtle scale- and orientation- invariant information
thereby generating a condensed response map. Then
condensed maps
that is
the outputs of the aforementioned Gabor convolution unit
are encoded within and across the channels. A local binary pattern encoding method is utilized to describe the texture distribution within every channel of a condensed map
and hash binary encoding is used to capture the relations across the map channels. The binarization during encoding enhances the robustness of representation to local changes. Subsequently
histogram calculation is applied to encoded maps to obtain statistical features
which are known as basic features. The aforementioned Gabor convolution unit
encoding module
and histogram calculation form a basic Gabor layer. In addition
this Gabor layer is provided with two extensive modules. The first module determines the invariance and global structures of texture distributions
and the second module enriches the pattern of response maps. The former restores and encodes the indices of max responses in the Gabor convolutional unit. The latter holistically learns a set of projection vectors from condensed response maps to construct a feature space. The texture representation not only becomes separable but also contains many patterns when it is projected in this feature space. Finally
the completed smoke features of a Gabor layer are generated by concatenating the basic and extensive features. The addition of extensive features enhances the robustness and discriminative capabilities of basic features because invariant texture structures
holistic information
and several patterns are characterized. A feedforward network termed GaborNet can be built by stacking several Gabor layers on top of one another. Consequently
the concatenation of features acquired from every Gabor layer constitutes multiscale
multi-orientation
and hierarchical features. The features become high level and slightly explicable with the deepening of the network. Thus
the extension
which explicitly improves the basic features
is conducted only on the first Gabor layer that possesses low-level features. In addition
holistic learning extension is not required in subsequent steps when the extension is implemented.
Result
2
This study conducted ablation experiments to gain insights on extensive features. Comparison experiments for smoke recognition were then conducted to present the performance of the proposed GaborNet. This algorithm utilizes texture representations to present smoke; thus
texture classification was conducted as a supplement to the experiment. Experimental results demonstrate that the proposed GaborNet achieves powerful generalization capability. Smoke features extracted by GaborNet decrease FARs and ERRs without dropping DRs
and the results of GaborNet rank first among state-of-the-art methods. The results of texture classification rank first and second in two standard texture datasets. In summary
the GaborNet provides better texture representation than most existing texture descriptors in smoke recognition and texture classification.
Conclusion
2
The proposed GaborNet extracts multiscale
multi-orientation
and hierarchical representations for textures; improves the performance of smoke recognition; and increases the accuracy of texture classification. Future studies should focus on eliminating the redundancy of features to gain compactness and in investigating and utilizing the relations between features in different layers to enhance transform invariance. This method is expected to be widely applied in real-time video smoke recognition.
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