Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison

For apple harvesting robot, it is difficult to acquire the coordinates of occluded apples accurately in natural scenes, which is important in implementing picking tasks. In this paper, a method on automatic recognition and localization of occluded apples was proposed. Firstly, an apple recognition algorithm based on K-means clustering theory was described. Secondly, convex hull information which was obtained by following the contours of extracted apple regions was used to extract the real apple edges. Finally, three points from these real edges were selected to estimate the centers and radius of apples. This algorithm was tested and compared with traditional Hough transform method (HT method) and contour curvature method (CC method) and 125 apple images were used to test the effectiveness of these methods. Four parameters including Segmentation Error (SE), False Positive Rate (FPR), False Negative Rate (FNR) and Overlap Index (OI) were used to evaluate the performance of these methods. Experimental results showed that SE of the presented method was decreased by 14.399 and 30.782 % when compared to CC method and HT method respectively, FPR by 7.234 and 11.728 % and OI was increased by 18.644 and 30.938 %. FNR of the proposed method was 0.912 % lower than CC method, while it was 5.869 % higher than HT method. The experimental results indicated that the proposed method could get much better localization rate than Hough transform method and contour curvature method, thus it could be concluded that the algorithm is an efficient means for the recognition and localization of occluded apples.