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首页> 外文会议>International conference on graphic and image processing >Vehicle License Plate Recognition in Dense Fog Based on Improved Atmospheric Scattering Model
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Vehicle License Plate Recognition in Dense Fog Based on Improved Atmospheric Scattering Model

机译：基于改进的大气散射模型的浓雾车辆牌照识别

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摘要

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.
机译：提出了一种基于改进的大气散射模型的有效方法来解决浓雾中车辆牌照的定位和识别问题。首先通过大礼帽变换和垂直边缘检测执行浓雾检测，然后将移动的车辆图像与交通视频图像分离。在将车辆图像分解为两层：结构层和纹理层之后，将发光层与结构层分离以获得背景层。通过执行均值合并和双三次插值算法，可以预测背景层的大气光图，同时通过灰色辉光层估计背景层的透射率，其灰度值可以通过线性映射进行更改。然后，根据改进的大气散射模型，可以通过融合恢复的背景层和优化的纹理层来获得最终的恢复图像。其次，通过一系列的形态学操作，关联域分析和各种验证来执行车牌定位。根据投影实现字符提取。最后，采用混合判别式受限局限博茨曼机器（HDRBM）的离线训练模式分类器来识别字符。据报道，在完整数据集上的实验结果表明，该方法能够在24h或一天之内的浓雾交通环境中实现较高的识别精度，并且能够很好地发挥作用。

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