针对基于Hough 变换类圆检测算法所需设置参数较多和基于距离直方图的算法计算量大等问题，提出了一种基于梯度的区域生长和距离直方图的快速圆检测方法(GHC)。该算法通过利用梯度模值和方向进行区域生长的方法得到若干圆弧线段支撑区域；选取弧线段上的三个坐标点求解该圆弧段对应的圆心和半径并求解出正方形适应区域；将每条圆弧线段上的所有点向其适应区域内各坐标点进行投影并统计距离的累加值；综合全图距离直方图，精确地求解出图像中包含各圆的圆心和半径并进行完整度校验。通过实验表明，相比基于距离直方图的圆检测算法(HBCD)和随机Hough变换算法(RHT)，该法对不同尺寸、完整度的单圆或多圆均有良好的检测效果，具有较强的稳健性和较小的空间、时间复杂度。

The most existing circle detectors based on Hough transform need to tune many parameters while the methods based on histogram are complex in computation and resource, thus a fast circle detector based on regiongrowing of gradient and histogram of Euclidean distance is presented to solve the above problems. The pixels′ gradient module and direction are computed in the first step and region-growing method is implemented to generate arc support regions. Three coordinates of each arc support region (ASR) are then selected to solve the center and radius of its corresponding circle and determine a square fitting area (SFA). Afterward, the Euclidean distances between every coordinates on each ASR and each coordinate of its ASR’s corresponding SFA are computed and recorded in a three dimensional accumulator. A histogram is used to count the frequency of the distances that participate in the accumulator and the parameters of each circle are acquired. A verification strategy of circular integrity is used to test the detection results. Compared with the histogram based circle detection (HBCD) and random Hough transform (RHT), experimental results indicate that the proposed algorithm is able to detect partial circles, multiple centers or circles in partial occlusion. This method has features of high speed, low consumption, wide range of application and strong anti-interference performance．