  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. With the proposed domain randomization
method, we could shrink the reality gap to a satisfactory level, achieving
86.32% and 97.38% mAP50 scores, respectively, in the case of zero-shot and
one-shot transfers, on our manually annotated dataset containing 190 real
images. Our solution fits for industrial use as the data generation process
takes less than 0.5 s per image and the training lasts only around 12 h, on a
GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar
classes of objects by having access to only one real image for training. To our
best knowledge, this is the only work thus far satisfying these constraints.

Robotics;Computer Vision and Pattern Recognition