线阵相机具有高敏感性、高分辨率和高动态性等特点,常用于运动物体成像。在动车组列车运行故障图像动态检测中,由于列车运行速度不是理想匀速通过线阵相机,所拍摄的图像会在列车运行方向上出现拉伸或压缩等变形现象,造成同一列车在不同时间所拍摄到的图像总数不一致,给后续目标自动定位、识别以及故障自动检测带来挑战。为解决图像之间未对齐的问题,提出了一种基于分块式的图像对齐方法。首先对图像进行分块处理、特征提取和匹配、特征量化,然后根据特征点之间的像素距离实现图像分块式校准,最后级联各个校准之后的图像块,完成目标图像与标准图像的对齐。实验结果表明,该方法对线阵相机所拍摄的动车组列车序列图像具有较好的对齐效果,可以精确定位序列图像中的目标。

Linear array camera is often used for the moving objects imaging because of the characteristics of high sensitivity, high pixel resolution, and wide dynamic range. In the dynamic detection of running fault image of the multiple units train, the images will stretch or compress in the direction of the train, because the train is not running at an ideal speed through the linear array camera. The number of images taken by the same train at different times is inconsistent, which brings challenges to automatic positioning, identification and automatic fault detection. In order to solve the unaligned problem, we present an block-based image registration method. The image is firstly divided into many sub-blocks, and then feature extraction, matching, and quantization. Each sub-block is corrected in accordance to the pixel distance of feature points. Finally, the correction of the entire images is fulfilled by concatenating the corrected sub-blocks, and the alignment of the target images with the standard image is completed. The experimental result demonstrates that this method has a better performance on alignment for multiple units train sequence images captured by linear array camera. It can accurately positioning the target in the sequence image.