Synthetic Data generation for Object Recognition and Pose Estimation
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Zhang, Desheng
Kong, Xiangbo
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The automotive industry is looking for methods to promote a flexible robotic assembly
on objects which are difficult for conventional robotic systems based on preprogramming.
Specifically, it is difficult to automate wire harness assembly onto
vehicles due to the limitation of current robotic assembly on pre-programed tasks
and the deformability of wire harnesses. In order to achieve flexible wire harness
assembly a well developed vision system. It is necessary for robots to perceive the
spatial information of wire harnesses and adapt their actions to handle the objects.
Computer vision techniques such as object recognition and pose estimation have
been widely adopted in robotics to promote the perception capabilities of robots
and the significant advancement in deep learning has remarkably promoted the research
in computer vision. However, a vast amount of time and human effort are
needed to collect and annotate the datasets for training deep learning models. In
recent years, researchers apply synthetic dataset in computer vision tasks, which
requires less human effort and promises good annotation quality. In this thesis,
the synthetic datasets of connectors are created by using a physically based rendering
method BlenderProc and procedures for data creation are provided for further
research and investigations. Then, the performance of synthetic datasets are evaluated
by object detection models(Yolov5 [22] and Yolov8 [23]) and a pose estimation
model (Wide Depth Range [20]). Also, the influences of applying domain randomization
methods(e.g. adding distarctors into the synthetic dataset) is discussed and
evaluated. By evaluating the experiment results, the study finds that similar objects
will cause mis-classification problems in connectors detection tasks, the domain gap
will lead to a poor performance on real data and adding distractors into synthetic
dataset can improve the robustness of the detectors. The study concludes with recommendations
for future research, such as using Generative Adversarial Networks
(GANs) to transfer the overall color and texture from the source images to the
target images, or apply a bilevel optimization approach. These kinds of methods
improve the domain gap between synthetic data and real data, thereby improving
the performance of models trained on synthetic datasets in the future
Beskrivning
Ämne/nyckelord
Synthesis dataset , object recognition , pose estimation , computer vision , machine learning , neural network , domain randomization , domain gap