Automatic generation of training data for semantic segmentation using game engine

Semantic segmentation is a traditional task in computer vision that has been widely used in many fields. However, there are still many challenges in generating large and diverse pixel-level labeled datasets, such as difficulty in data collection, inaccurate manual annotation, and high cost. To address these issues, this paper proposes an innovative method based on a game engine for automatically generating semantic segmentation training data. By flexibly controlling the generation of scene objects and camera configurations, this method significantly simplifies the data collection and annotation process, effectively expands the scale of training data, and improves model performance. Experimental results demonstrate the effectiveness of the method in computer-generated images and real-world scene images, verifying its feasibility in improving the performance of semantic segmentation models. This paper provides a reliable alternative to the traditional semantic segmentation data generation process and lays the foundation for improving the generalization ability of models.