用新模型让机器人在遮挡下也能准确识别完整果实，提升采摘成功率。
GDA-YOLO11: Amodal Instance Segmentation for Occlusion-Robust Robotic Fruit Harvesting
- 基于改进的YOLO11架构与新型损失函数，实现遮挡下的完整果实分割
- 在遮挡场景下最高采摘成功率达92.59%，中高遮挡提升3.5%
- 首次实现在真实柑橘采摘中应用无遮挡实例分割，适合农业机器人研发
遮挡仍是机器人采摘水果中的关键挑战，未检测或定位不准会导致严重作物损失。为此，我们提出一种基于新式无遮挡分割模型GDA-YOLO11的采摘框架，该模型结合架构优化与更新的非对称掩码损失。模型在修改后的公开柑橘数据集上训练，并在基础数据集及不同遮挡水平的敏感子集上评估。框架通过GDA-YOLO11推断包含不可见区域的完整果实掩码，再利用欧氏距离变换估算拾取点，并投影至3D坐标执行采摘。实验在模拟遮挡环境的真实柑橘上进行。据我们所知，这是首次在机器人采摘中实现无遮挡实例分割的实际演示。GDA-YOLO11取得0.844精度、0.846召回率、mAP@50为0.914、mAP@50:95为0.636，较YOLO11n分别提升5.1%、1.3%和1.0%。在零至高遮挡水平下，采摘成功率分别为92.59%、85.18%、48.14%和22.22%，中高遮挡下提升3.5%。结果表明，GDA-YOLO11显著增强遮挡鲁棒性分割能力并实现感知到动作的高效集成，推动农业自主系统发展。
原文摘要 · Abstract (English)
Occlusion remains a critical challenge in robotic fruit harvesting, as undetected or inaccurately localised fruits often results in substantial crop losses. To mitigate this issue, we propose a harvesting framework using a new amodal segmentation model, GDA-YOLO11, which incorporates architectural improvements and an updated asymmetric mask loss. The proposed model is trained on a modified version of a public citrus dataset and evaluated on both the base dataset and occlusion-sensitive subsets with varying occlusion levels. Within the framework, full fruit masks, including invisible regions, are inferred by GDA-YOLO11, and picking points are subsequently estimated using the Euclidean distance transform. These points are then projected into 3D coordinates for robotic harvesting execution. Experiments were conducted using real citrus fruits in a controlled environment simulating occlusion scenarios. Notably, to the best of our knowledge, this study provides the first practical demonstration of amodal instance segmentation in robotic fruit harvesting. GDA-YOLO11 achieves a precision of 0.844, recall of 0.846, mAP@50 of 0.914, and mAP@50:95 of 0.636, outperforming YOLO11n by 5.1%, 1.3%, and 1.0% in precision, mAP@50, and mAP@50:95, respectively. The framework attains harvesting success rates of 92.59%, 85.18%, 48.14%, and 22.22% at zero to high occlusion levels, improving success by 3.5% under medium and high occlusion. These findings demonstrate that GDA-YOLO11 enhances occlusion robust segmentation and streamlines perception-to-action integration, paving the way for more reliable autonomous systems in agriculture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。