A detection method for occluded and overlapped apples under close-range targets

Accurate and rapid identification and location of apples contributes to speeding up automation harvesting. However, in unstructured orchard environments, it is common for apples to be overlapped and occluded by branches and leaves, which interferes with apple identification and localization. In order to quickly reconstruct the fruits under overlapping and occlusion conditions, an adaptive radius selection strategy based on random sample consensus algorithm (ARSS-RANSAC) was proposed. Firstly, the edge of apple in the image was obtained by using image preprocessing method. Secondly, an adaptive radius selection strategy was proposed, which is based on fruit shape characteristics. The fruit initial radius was obtained through horizontal or vertical scanning. Then, combined with RANSAC algorithm to select effective contour points by the determined radius, and the circle center coordinates were obtained. Finally, fitting the circle according to the selected valid contour and achieving the recognition and localization of overlapped and occluded apples. 175 apple images with different overlaps and branches and leaves occlusion were applied to verify the effectiveness of algorithm. The evaluation indicators of overlap rate, average false-positive rate, average false-negative rate, and average segmentation error of ARSS-RANSAC were improved compared with the classical Hough transform method. The detection time of a single image was less than 50 ms, which can meet requirements of real-time target detection. The experimental results show that the ARSS-RANSAC algorithm can quickly and accurately identify and locate occluded and overlapped apples and is expected to be applied to harvesting robots of apple and other round fruits.