Creating high-quality computer vision datasets that accurately reflect real-world scenarios can be a challenge. To address this challenge, NLR has developed a virtual sandbox environment that allows us to generate realistic 3D objects from various angles. This environment also enables us to place objects in diverse settings, such as different weather conditions, ensuring that our datasets are robust and applicable to specific real-world use cases.
Our virtual sandbox environment offers numerous benefits, including the ability to generate realistic 3D objects, detect real-world objects from generated data, automate the annotation process of dataset generation, and provide practically unlimited options for varying picture angles, environments, and object types. Additionally, our team has expertise in computer vision algorithms such as YOLO (You Only Look Once) and data augmentation techniques.
NLR can support you with:
- Overcoming the challenges of scarce, low-quality, or labour-intensive computer vision datasets.
- Solving problems that have a strong visual component.
- Detecting objects that are challenging to capture or record.
- Generating datasets for computer vision applications.
- Detecting objects in dynamic or changing environments.
- Creating virtual sandboxes where 3D objects can be generated realistically.
- Detecting real-world objects from generated data.
- Automating the annotation process of dataset generation.
- Providing flexible dataset creation options with varying picture angles, environments, and object types.
- Applying knowledge of computer vision algorithms like YOLO.
- Utilising data augmentation techniques to enhance dataset quality.
