Towards a Cloud Robotics Platform

Abstract

本文提出了一种可重用、高效的基于两阶段深度学习的工业环境表面缺陷检测方法。为了同时兼顾效率和准确性，我们提出了一种新的分割阶段（stage1）和检测阶段（stage2）的组合，这两个阶段分别由两个完全卷积网络（FCN）组成。在分割阶段，我们使用一个轻量级的FCN进行空间密集的像素级预测，从而粗略、快速地推断出缺陷区域。这些预测出的缺陷区域作为stage2的初始化，指导检测过程以细化分割结果。我们还使用了一种不寻常的训练策略：从图像中裁剪出补丁进行训练。这种策略在培训数据可能不足的工业检验中有很大的用处。我们将通过分析DAGM2007数据集的性能来验证我们的发现 In this contribution we introduce the Shape Flow algorithm (SF), a novel method for spatio-temporal 3D pose estimation of a 3D parametric curve. The SF is integrated into a tracking system and its suitability for tracking human body parts in 3D is examined. Based on the example of tracking the human hand-forearm limb it is shown that the use of two SF instances with different initialisations leads to an accurate and temporally stable tracking system. In our framework, the temporal pose derivative is available instantaneously, therefore we avoid delays typically encountered when filtering the pose estimation results over time. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. Experimental investigations are performed on 5 real-world test sequences showing 3 different test persons in an average distance of 1.2–3.3 m to the camera in front of cluttered background. We achieve typical pose estimation accuracies of 40–100 mm for the mean distance to the ground truth and 4–6 mm for the pose differences between subsequent images.