Copy and Paste Augmentation for Deformable Wiring Harness Bags Segmentation

• Main Authors: Zagar, Bare Luka, Caporali, Alessio, Szymko, Amadeusz, Kicki, Piotr, Walas, Krzysztof, Palli, Gianluca, Knoll, Alois C
• Format: Conference Proceeding
• Language: English
• Subjects: Data Augmentation; Deformable Objects; Industrial Manufacturing; Mechatronics; Pipelines; Production facilities; Segmentation; Semantic segmentation; Transfer learning; Wires; Wiring
• ISSN: 2159-6255
• DOI: 10.1109/AIM46323.2023.10196168

Wiring harnesses, i.e. a collection of electrical cables organized into branches, are vastly present in the automotive industry. Moreover, the number of wires and overall weight of automotive wiring harnesses are steadily increasing over time. Deformable wiring harness bags were introduced by manufacturers to simplify assembly operations. However, this task is still entirely performed manually by human labor. Despite the efforts, the degree of automation in wiring harness assembly is still close to zero. Due to the lack of task-specific datasets, modern state-of-the-art computer vision approaches are not commonly employed in the wiring harness industrial processes. In this work, we propose an approach to generate a dataset of a specific object of interest, i.e. deformable wiring harness bags, with minimal effort employing the copy and paste technique. The obtained dataset is validated on the semantic segmentation task in a real-world test setup, consisting of laboratory and automotive factory environments. An overall IoU of 53.8% and Dice score of 65.6% is obtained, demonstrating the capability of the proposed method.