Research on Target-Region Segmentation and Robust Sphere Fitting for RGB-D Apple Picking-Point Localization

To address the challenge of apple picking-point localization under severe occlusion and complex backgrounds in dwarf rootstock high-density orchards, this paper proposes a target-region-segmentation-based 3D localization method in which the fruit sphere center is defined as the picking point. A lightweight occlusion-aware semantic segmentation network, OA-LiteSegNet, is developed to generate accurate foreground masks for partially visible apples. Guided by the segmentation result, a local target-region point cloud is reconstructed from the depth map and further processed by robust sphere fitting to estimate the apple sphere center and fruit diameter. Experimental results show that OA-LiteSegNet achieves 90.35% mIoU and 76.27% BF1 with only 5.92 M parameters and 11.30 GFLOPs, while reaching an inference speed of 128.40 FPS, achieving a favorable accuracy–efficiency trade-off among the compared mainstream models. The proposed 3D localization method achieves an average sphere-center error of 2.88 mm, which is 68.4% lower than that of the conventional bounding-box-center back-projection method (9.11 mm), and maintains stable performance across different occlusion types. These results demonstrate the potential of the proposed method for real-time and accurate apple localization under controlled laboratory occlusion scenarios, while further field validation in real orchards remains necessary.