高铁轮毂表面缺陷的视觉显著性超像素图像检测方法

针对高铁轮毂表面缺陷实时在线检测问题，提出一种基于视觉显著性注意机制的超像素自适应检测方法。首先采用同态滤波器对缺陷图像进行预处理，去除环境光污染噪声引起的图像亮度分布不均匀问题，构建轮毂表面缺陷图像的谱残差视觉注意模型，之后采用超像素分割算法对缺陷显著性图像进行自适应阈值分割，标记出高铁轮毂表面缺陷的二维空间位置，实现轮毂表面缺陷的边界检测和形态估计。本文方法在高铁轮毂表面缺陷检测实验平台上进行了实验验证，结果表明：该方法能够有效抑制图像分割中的过分割问题，对缺陷的边界信息提取准确，鲁棒性较好。

Research on Visual saliency Superpixel Image Detection Method for Surface Defect of High-speed Rail Hub

In order to real-time on-line detection of surface defects of high-speed rail hub, a superpixel adaptive detection method based on visual saliency attention mechanism is proposed. Firstly, homomorphic filter was used to preprocess defect image to remove the uneven distribution of image brightness caused by ambient light pollution noise, and a spectral residual visual attention model of defect image on high-speed rail hub surface was constructed. Then, superpixel segmentation algorithm was used to segment the defect saliency image by adaptive threshold. The two-dimensional space location of surface defects of high-speed rail hub was marked, and the boundary detection and shape estimation of surface defects of on high-speed rail hub were realized. The experimental verification of the method was carried out on the experimental platform for surface defect detection of high-speed railway hub. The results show that the method can effectively restrain over-segmentation in image segmentation, extract the boundary information of defects accurately and have good robustness.