Electrical cables consist of numerous wires, the three-dimensional (3D) shape of which significantly impacts the cables’ overall properties, such as bending stiffness. Although X-ray computed tomography (CT) provides a non-destructive method to assess these properties, accurately determining the 3D shape of individual wires from CT images is challenging due to the large number of wires, low image resolution, and indistinguishable appearance of the wires. Previous research lacked quantitative evaluation for wire tracking, and its overall accuracy heavily relied on the accuracy of wire detection. In this study, we present a long short-term memory-based approach for wire tracking that improves robustness against detection errors. The proposed method predicts wire positions in subsequent frames based on previous frames. We evaluate the performance of the proposed method using both actual annotated cables and artificially noised annotations. Our method exhibits greater tracking accuracy and robustness to detection errors compared with the previous method.
Electrical cables consist of numerous wires. The 3D shape of individual wires in the cables significantly affects their characteristics. With the increasing diversity of wire structures, understanding the 3D shape of each wire in an electrical cable is crucial for analyzing characteristics such as bending stiffness. To accurately estimate the bending stiffness of actual electrical cables, a detailed 3D representation of each wire is required. Therefore, it is important to obtain the 3D shape of actual electrical cable wires using non-destructive inspection. In this study, we propose a new method to associate wires using particle tracking techniques. The proposed method performs wire tracking in two steps, linking detected wire positions between adjacent frames and connecting segmented wires across frames. The effectiveness of the proposed method was evaluated quantitatively and qualitatively. In the quantitative evaluation, the correctness of the tracking was evaluated using the data with artificial noise added to the real data detection results. The proposed method achieves highly accurate wire tracking when there is little undetected noise, and shows robustness to over-detection.
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