Wire-tracking of bent electric cable using X-ray CT and deep active learning.

Yutaka Hoshina, Takuma Yamamoto, Shigeaki Uemura
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Abstract

We have demonstrated a quantification of all component wires in a bent electric cable, which is necessary for discussion of cable products in actual use cases. Quantification became possible for the first time because of our new technologies for image analysis of bent cables. In this paper, various image analysis techniques to detect all wire tracks in a bent cable are demonstrated. Unique cross-sectional image construction and deep active learning schemes are the most important items in this study. These methods allow us to know the actual state of cables under external loads, which makes it possible to elucidate the mechanisms of various phenomena related to cables in the field and further improve the quality of cable products.

利用 X 射线 CT 和深度主动学习对弯曲电缆进行电线跟踪。
我们展示了弯曲电缆中所有元件导线的量化,这对于讨论实际应用案例中的电缆产品非常必要。由于我们采用了对弯曲电缆进行图像分析的新技术,量化首次成为可能。本文展示了检测弯曲电缆中所有电线轨迹的各种图像分析技术。独特的横截面图像构建和深度主动学习方案是本研究的重中之重。通过这些方法,我们可以了解电缆在外部载荷作用下的实际状态,从而有可能在现场阐明与电缆相关的各种现象的机理,并进一步提高电缆产品的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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