复杂背景图像下基于边缘点校验的圆检测方法

Circle Detection Method Based on Edge Point Verification Applied to Complex Background Image

针对传统圆检测方法在复杂背景下检测精度低、误识别及漏识别率高、可靠性差的缺点,提出一种适应于复杂背景图像下的圆检测方法。根据半径搜索范围,利用三级筛选法选出候选圆,对所有的候选圆统计边缘点个数,将统计结果除以半径,根据此值对候选圆进行筛选。依据统计结果对圆排序,根据设定的最小圆心距及最小半径差阈值消除因峰值扩散产生的干扰圆。实验结果表明,与传统梯度Hough变换相比,该方法的误识别率和漏识别率分别降低24%和8%,检测结果更精确,在复杂背景下的可靠性更高。

Traditional circle detection method has several limitations in complex background, such as low accuracy, high error detection rate, high leak identification rate and low reliability. To solve these problems, a circle detection method which can be applied to complex background is proposed. According to radius search range, it picks out candidate circles in the way of three level screening method. It calculates the number of each candidate circle edge point and divides it by circle radius, and then picks out real circles based on this result. It sorts all circles and deletes interference circles according to the minimum distance and minimum radius difference threshold between circles. Experimental results show that, in comparison to traditional Hough gradient transform method, the proposed method can detect circle more accurately and reliably with error detection rate reduced by 24% , and leak identification rate reduced by 8% ,even in complex background.