Our Approaches
We use a data-centric approach to train object detection models, ensuring high-quality and diverse datasets through synthetic image generation. Our process includes 3D rendering to create realistic scenes and generative AI to enhance these images with real-world characteristics. This comprehensive dataset preparation enables our models to perform well in detecting and localizing objects under various conditions.
We start by identifying the specific object detection needs of our clients, including the types of objects and scenarios they encounter. Using CAD models and advanced rendering techniques, we generate synthetic datasets that accurately represent these conditions. Our generative AI techniques add realistic details such as lighting variations, textures, and occlusions, ensuring the models are well-equipped to handle real-world challenges.
Our approach also includes continuous model validation and optimization, using both synthetic and real-world data. This ensures that our object detection systems remain accurate and reliable over time. We provide tools for easy integration with existing workflows, enabling seamless adoption and deployment of our solutions.
