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In this article, an innovative algorithm for instance segmentation of wires called Ariadne+ is presented. Although vastly present in many manufacturing environments, the perception and manipulation of wires is still an open problem for robotic applications. Wires are deformable linear objects lacking of any specific shape, color, and feature. The proposed approach uses deep learning and standard computer vision techniques aiming at their reliable and time effective instance segmentation. A deep convolutional neural network is employed to generate a binary mask showing where wires are present in the input image, then the graph theory is applied to create the wire paths from the binary mask through an iterative approach that aims to maximize the graph coverage. In addition, the B-Spline model of each instance, useful in manipulation tasks, is provided. The approach has been validated quantitatively and qualitatively using a manually labeled test dataset and by comparing it against the original Ariadne algorithm. The timings performances of the approach have been also analyzed in depth.

Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires