Deep Learning-Based Connector Detection for Robotized Assembly of
  Automotive Wire Harnesses
Hao Wang Björn Johansson
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.