###### Abstract  
This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires substantial volumes of training data with associated metadata. Synthetic data can solve this by providing unlimited desired training data with automatic generation. However, the main challenge is creating a balanced dataset that closes the reality gap and generalizes well when deployed in the real world. A state-of-the-art game engine, Unreal Engine 4, was used to approach the challenge of generating a photorealistic dataset for deep learning model training. In addition, a comprehensive domain randomized environment was implemented to create a robust dataset that generalizes the training data well. The randomized environment was reinforced by adding high-dynamic-range image scenes. Finally, a modern neural network was used to train the object detection system, providing a robust framework for an adaptive and self-learning model. The final models were deployed in simulation and in the real world to evaluate the training. The results of this study show that it is possible to train a real-world object detection system on synthetic data. However, the models showcase a lot of potential for improvements regarding the stability and confidence of the inference results. In addition, the paper provides valuable insight into how the number of assets and training data influence the resulting model.  
computer vision; deep learning; domain randomization; object detection; NDDS; PyTorch; sim2 real; synthetic data; Unreal Engine; YOLOv5 +
Footnote †: journal: Applied  
By combining these technologies, this paper contributes positively toward using purely synthetic data in object detection systems.  
By successfully utilizing this, labor-intensive tasks can be automated, edge cases within computer vision can be trained, and progress will be accelerated.  
When creating an accurate object detection system, it is necessary to provide a vast number of data consisting of the preferable objects. The objects of interest must be annotated with a bounding box for the machine learning software to know what, where, and how many objects to detect. However, manual annotation of data can be time-consuming and challenging. One alternative is to utilize existing datasets from different distributors, such as ImageNet (9.2M images) [2] and Microsoft COCO (328k images) [3]. The traditional way of annotating a bounding box around an object consists in manually marking the imaginary corners of the box. Furthermore, the box must be adjusted to eliminate the space between the object and the box walls. Different systems make it possible to label data faster. One example is the fast box drawing technique \"Extreme Clicking\" by [PERSON] et al. [4]. The system simplifies the annotation process by marking the outer parts of the object (top, bottom, left, right).  
This paper proposes an end-to-end training framework for training object detection models such as YOLOv5. Our framework uses UE4 for rendering high-quality scenes and NDDS as a dataset synthesizer for domain randomization. These tools combined create photorealistic virtual environments with great customization and enable rapid prototyping and training of object detection algorithms in nearly any industrial setting.