为解决岩碴和背景颜色相近、边缘不规则、堆积重叠、单个岩石像素不一致等导致的图像分割识别困难问题，提出一种基于距离及边界长度的凹点匹配算法。首先，对图像进行直方图均衡化、高斯降噪等处理，增强图像对比度并抑制部分噪声； 其次，利用像素阈值分割法对图像进行二值化处理并提取图像轮廓； 再次，利用矢量夹角法对提取的岩碴轮廓进行凹点检测，并基于岩碴图像特征，设计了一种新的凹点匹配准则； 最后，选取实际工程中的岩碴图片对所提方法进行验证，结果表明，该方法能够较准确地获取凹点对，对于碴片图像轮廓分割的效果，像素准确率达到88.9%，IoU达到82.7%。

A concave point matching algorithm based on distance and boundary length is proposed to address the difficulty in image segmentation and recognition caused by similar color of rock ballast and background, irregular edge, stacking and overlapping, and inconsistency of single rock pixel. First, the image is processed by histogram equalization and Gaussian noise reduction to enhance the image contrast and suppress part of the noise. Second, the pixel threshold segmentation method is employed to binary the image and extract the image outline. Third, the vector angle method is employed to detect the concave points of the extracted rock ballast contour, and a new concave point matching criterion is designed based on the characteristics of rock ballast image. Finally, the proposed method is validated by selecting rock ballast images from actual projects. The results show that the method can obtain the concave point pairs more accurately, the pixel accuracy rate and the IoU in contour segmentation effect of the ballast image reach 88.9% and 82.7%, respectively.