工业应用中包裹箱检测算法的比较评估

E. Fontana, William Zarotti, Dario Lodi Rizzini
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引用次数: 2

摘要

工业物流可以从对象感知中受益,从而对货物进行灵活有效的管理。本文阐述并实验比较了两种用于工业脱垛任务的深度图像包裹盒检测方法。基于模型的方法根据曲率等几何特征检测输入点云中的聚类,并对候选对象进行聚合。基于学习的方法依赖于最先进的掩码R-CNN,该掩码R-CNN已经在缺失测量值的获取数据集上进行了重新训练。通过标准几何配准来评估目标物体的姿态。在采集数据集上的实验表明了这两种方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Assessment of Parcel Box Detection Algorithms for Industrial Applications
Industrial logistics may benefit from object perception to perform flexible and efficient management of goods. This paper illustrates and experimentally compares two approaches to parcel box detection in depth images for an industrial depalletization task. The model-based method detects clusters in the input point cloud according to curvature and other geometric features, and aggregates the candidate objects. The learning-based method relies on the state-of-the-art Mask R-CNN, which has been re-trained on an acquired dataset with missing measurements. The target object poses are evaluated through standard geometric registration. The experiments on acquired datasets show the feasibility of the two approaches.
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