Object Detection Using Sim2Real Domain Randomization for Robotic Applications

Understand

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings.

• One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications.
• In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated.
• Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects.