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中文摘要：
针对目前钢轨表面缺陷检测的速度，精度较低，分类较难的现状，提出一种基于视觉注意力和 PLSA 模型的钢轨表面缺陷检测方法；结合亮度和纹理特征的视觉注意力模型检测钢轨表面缺陷，提取原图像的缺陷区域，并采用 PLSA 模型对提取的缺陷进行分类。实验结果表明：所提出的方法提高了检测及分类的速度与精度，能满足钢轨表面缺陷检测的要求。
英文摘要：
Aimed at the status quo fact that speed,accuracy and classification of rail surface defects are relative-ly low,this paper proposed a rail surface defect detection method based on visual attention and PLSA model method which is a combination of brightness and texture of the visual attention model to detect surface defects of rail,extraction of defects region of the original image,and uses the PLSA model to classify the defects.The ex-perimental results show that the proposed method,improves the speed,precision and classification of rail surface defects detection and can meet the requirements of the rail surface defects detection.
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