基于距离变换和凹点分析的粘连樱桃快速分割方法

Fast Segmentation Method for Overlapping Cherries Based on Distance Transform and Curvature Concave Point Analysis

摘要:

针对樱桃分选线上因果实体积较小易发生粘连,影响分选效率的问题,提出了一种结合质心检测与凹点匹配的快速分割算法。首先,利用距离变换结合邻域极大值方法精确提取樱桃质心,并根据提取的质心划分粘连区域。然后,采用凹点分析法处理不同粘连情况的区域,利用凹点匹配筛选得到预备分割直线。为进一步提高分割精度,对可能影响果体形状的分割线周围区域进行USM(UnsharpMask)锐化,并结合梯度信息进行精细分割。试验结果表明,本文提出的质心检测方法精确率和召回率分别为98.54%和97.93%,较距离变换腐蚀法提升了5.64、11.18个百分点;凹点检测精确率和召回率分别为92.95%和94.76%,较凸包法提升了3.52、20.58个百分点;粘连分割的效果优于分水岭方法,能更精确地处理不同粘连情况,计算耗时6ms。本文方法具有较高的准确率和较短的耗时,能够满足樱桃分选生产线的实时处理需求。

Abstract:

A rapid separation algorithm was proposed to solve the issue of cherry overlapping on sorting lines caused by cherries??small size, which negatively impacted sorting efficiency. The algorithm aimed to improve the accuracy and speed of cherry separation, addressing the problem of different overlapping scenarios: the proposed algorithm efficiently combined centroid detection and concave point matching to separate cherries. Firstly, the foreground image was expanded by using edge tangent extension and the distance transform values of the cut fruit bodies were accurately calculated. The centroid of the cherries was then extracted by using the distance transform and neighborhood maximum methods. The thresholding and small window maximum methods were used to accelerate centroid extraction. Curvature analysis and point clustering methods were employed to accurately detect the concave points of the inner and outer contours for concave point extraction. These concave points were used to handle areas with varying overlapping situations and applied concave point matching to filter out preliminary separation lines. Finally, to enhance segmentation accuracy, the regions around the separation lines, which might affect the fruit’s shape, were sharpened by using Unsharp Mask ( USM), and refined segmentation was performed by incorporating gradient information, resulting in more accurate overlapping separation curves. Experimental results showed that the centroid detection method proposed achieved precision and recall rates of 98. 54% and 97. 93% , respectively, improving by 5. 64 and 11. 18 percentage points compared with that of the distance transform erosion method. The precision and recall rates for the concave point detection were 92. 95% and 94. 76% , respectively, showing improvements of 3. 52 and 20. 58 percentage points over that of the convex hull method. The proposed algorithm demonstrated greater precision in separating cherries under various overlapping conditions than watershed-based separation methods. On an AMD Ryzen 5 3600 6-Core Processor, the segmentation of 989 pixel × 200 pixel images took 6 ms. The proposed method achieved high detection accuracy and significantly reduced processing times, making it suitable for real-time processing in cherry sorting production lines. It effectively met the demands of both accuracy and efficiency for the industry.