The shift towards electrification and autonomous driving in the automotive industry results in more and more automotive wire harnesses being installed in modern automobiles, which stresses the great significance of guaranteeing the quality of automotive wire harness assembly. The mating of connectors is essential in the final assembly of automotive wire harnesses due to the importance of connectors on wire harness connection and signal transmission. However, the current manual operation of mating connectors leads to severe problems regarding assembly quality and ergonomics, where the robotized assembly has been considered, and different vision-based solutions have been proposed to facilitate a better perception of the robot control system on connectors. Nonetheless, there has been a lack of deep learning-based solutions for detecting automotive wire harness connectors in previous literature. This paper presents a deep learning-based connector detection for robotized automotive wire harness assembly. A dataset of twenty automotive wire harness connectors was created to train and evaluate a two-stage and a one-stage object detection model, respectively. The experiment results indicate the effectiveness of deep learning-based connector detection for automotive wire harness assembly but are limited by the design of the exteriors of connectors.

翻译：汽车行业向电气化和自动驾驶的转型导致现代汽车中安装的汽车线束数量不断增加，这凸显了保障汽车线束装配质量的重要性。由于连接器在线束连接和信号传输中的关键作用，连接器的插接是汽车线束最终装配中的核心环节。然而，当前连接器插接的人工操作在装配质量和人体工程学方面引发严重问题，因此机器人化装配已被纳入考量，且多种基于视觉的解决方案被提出以提升机器人控制系统对连接器的感知能力。然而，现有文献中缺少基于深度学习的汽车线束连接器检测方案。本文提出了一种基于深度学习的连接器检测方法，用于汽车线束的机器人化装配。通过构建包含二十种汽车线束连接器的数据集，分别训练并评估了一个两阶段目标检测模型和一个单阶段目标检测模型。实验结果表明，基于深度学习的连接器检测在汽车线束装配中具有有效性，但其性能受限于连接器外壳的几何设计。