FASTDLO: Fast Deformable Linear Objects Instance Segmentation
FASTDLO: Fast Deformable Linear Objects Instance Segmentation
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FASTDLO：快速可变形线性物体实例分割
DOI：
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发表时间：
2022
影响因子：
5.2
通讯作者：
G. Palli
中科院分区：
文献类型：
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作者：
Alessio Caporali;Kevin Galassi;R. Zanella;G. Palli
提出了一种快速、准确分割可变形线状目标的方法FASTDLO。深度卷积神经网络用于背景分割，生成隔离图像中DLO的二进制掩码。然后，用一个离散化算法处理得到的掩模，用一个基于相似性的网络求解不同DLO之间的交集。除了通常的逐像素颜色映射图像外，FASTDLO还使用一系列2D坐标描述每个DLO实例，例如，可以使用样条曲线对DLO实例进行建模。综合生成的数据用于训练数据驱动的方法，避免昂贵的收集和注释的真实的数据。FASTDLO与特定于DLO的方法和通用深度学习实例分割模型进行了实验比较，实现了更好的整体性能和高于20 FPS的处理速率。
In this paper, an approach for fast and accurate segmentation of Deformable Linear Objects (DLOs) named FASTDLO is presented. A deep convolutional neural network is employed for background segmentation, generating a binary mask that isolates DLOs in the image. Thereafter, the obtained mask is processed with a skeletonization algorithm and the intersections between different DLOs are solved with a similarity-based network. Apart from the usual pixel-wise color-mapped image, FASTDLO also describes each DLO instance with a sequence of 2D coordinates, enabling the possibility of modeling the DLO instances with splines curves, for example. Synthetically generated data are exploited for the training of the data-driven methods, avoiding expensive collection and annotations of real data. FASTDLO is experimentally compared against both a DLO-specific approach and general-purpose deep learning instance segmentation models, achieving better overall performances and a processing rate higher than 20 FPS.