基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法

尉维洁; 齐健1; 周南1; 刘化男1; 高会颖2

基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法

引用本文：尉维洁，齐健1，周南1，刘化男1，高会颖2.基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法[J].计算技术与自动化,2024,(2):116-122

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作者	单位
尉维洁，齐健1，周南1，刘化男1，高会颖2	（1.国家能源集团新疆能源有限责任公司洗选中心，新疆维吾尔自治区乌鲁木齐 830000；2.天津美腾科技股份有限公司，天津 300000)

中文摘要:精准识别铁路车号可以为煤厂装车提供依据，从而保证装车环节高效顺利地完成。为此，提出了基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法。首先，利用高速摄像机设备采集原始的车号图像，并利用索贝尔算子检测图像边界；然后，根据列车车号的字体笔画宽度特点，采笔画宽度变换算法定位确定图像中的车号区域，并利用LBP算法提取车号区域内的特征；最后，将提取的特征输入到深度信任网络模型中，在训练网络模型并不断更新参数后，准确识别车号图像。实验表明：该方法能够精准识别乌东选煤厂铁路列车车号图像。在深度信任网络模型中，当受限玻尔兹曼机网络为4层、隐含层节点个数为128个时，该模型的分类识别能力最强，训练损失最小，性能最佳。

中文关键词:深度信任网络边界检测车号定位图像识别笔画宽度变换特征提取

Image Recognition Method of Railway Car Number of Wudong Coal Preparation Plant Based on Deep Trust Network Model

Abstract:Accurate identification of railway vehicle number can provide basis for coal plant loading, thus ensuring the efficient and smooth completion of the loading process. Therefore, a method of railway vehicle number image recognition based on deep trust network model in Wudong Coal Preparation Plant is proposed. Firstly, the original vehicle number image is collected by high-speed camera equipment, and the image boundary is detected by Sobel operator; Then, based on the font stroke width characteristics of the train number, a stroke width transformation algorithm is used to locate and determine the train number area in the image, and the LBP algorithm is used to extract features within the train number area; Finally, the extracted features are input into the deep trust network model. After training the network model and constantly updating the parameters, the vehicle number image is accurately recognized. The experiment shows that this method can accurately recognize the train number image of Wudong Coal Preparation Plant. In the deep trust network model, when the restricted Boltzmann network is 4 layers and the number of hidden layer nodes is 128, the model has the strongest classification recognition ability, the minimum training loss and the best performance.

keywords:deep trust network boundary detection vehicle number positioning image recognition stroke width change feature extraction

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