A Method for Target Detection Based on Synthetic Samples of Digital Twins

Abstract

Target detection technology in the field of machine vision plays a vital role in industrial production and manufacturing. In industrial production, productivity can be improved by accurate target detection. To implement this technology, many enterprises must manually clean and label a huge dataset. Meanwhile, it is a huge challenge for enterprises to obtain the dataset because of enterprise data privacy and security constraints. This paper proposes a method for rapidly generating synthetic samples based on digital twins to address this challenge. First, the virtual environment is utilized to replicate the real detecting environment, generating a variety of sample photos. The three-dimensional coordinates of the target object are then extracted in the virtual scene. Subsequently, an annotation method is designed for synthetic samples obtained from the virtual scene, utilizing principles of three-dimensional coordinate transformation and perspective coordinate transformation. This method efficiently produces numerous labeled samples with diverse annotations. Ultimately, the model performs detection tasks in the actual world using the synthetic samples as training data. The experimental results show that the synthetic samples created by this method based on digital twins can substitute real samples and effectively identify target objects during actual detection tasks. This paper proposes a unique strategy for synthetic samples that reduces sample collection costs and privacy risks, thereby addressing the limitations of machine vision detection technology induced by sample limitations.

Keywords

• Digital twins
• coordinates transformation
• automatic annotation
• synthetic samples
• target detection