Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
复制标题
将联动机构的手绘草图转换为数字表示形式
DOI：
10.1115/1.4064037
复制
发表时间：
2024
影响因子：
3.1
通讯作者：
Purwar, Anurag
中科院分区：
文献类型：
--
作者：
Nurizada, Anar;Purwar, Anurag
介绍了一种基于手绘草图的n杆平面连杆机构的深度神经网络交互数字变换与仿真的新方法。与单纯依赖计算机视觉不同，我们的方法将连杆机构的拓扑知识与卷积深度神经网络的结果相结合。这为识别手绘草图创建了一个框架。我们生成一个合成图像的数据集，它类似于连杆机构的手绘草图。接下来，我们微调最先进的深度神经网络，以使用构建块来检测离散对象，这些构建块表示这些草图中不同位置、大小和方向的关节和链接。然后对检测到的物体进行拓扑分析，以构建草图机构的运动学模型。实验结果证明了该算法在处理手绘草图并将其转换为数字表示方面的有效性。这对于改进平面机构的沟通、分析、组织和分类具有实际意义。
This paper introduces a new method using deep neural networks for the interactive digital transformation and simulation of n-bar planar linkages, which consist of revolute and prismatic joints, based on hand-drawn sketches. Instead of relying solely on computer vision, our approach combines topological knowledge of linkage mechanisms with the outcomes of a convolutional deep neural network. This creates a framework for recognizing hand-drawn sketches. We generate a dataset of synthetic images that resemble hand-drawn sketches of linkage mechanisms. Next, we fine-tune a state-of-the-art deep neural network to detect discrete objects using building blocks that represent joints and links in various positions, sizes, and orientations within these sketches. We then conduct a topological analysis on the detected objects to construct a kinematic model of the sketched mechanisms. The results demonstrate the effectiveness of our algorithm in handling hand-drawn sketches and converting them into digital representations. This has practical implications for improving communication, analysis, organization, and classification of planar mechanisms.