Bridging the Reality Gap in Industrial Synthetic Datasets with Aging Texture Simulation

As industries increasingly rely on computer vision applications, the demand for robust object detection models grows. Synthetic datasets offer a promising solution by providing diverse and accurate data scenarios, enhancing the performance of these models. In this study, we tackle one aspect of the ongoing challenge to bridge the reality gap between synthetic and real images in industrial applications. We incorporated textures that accurately replicate signs of usage on industrial assets. Further-more, we conducted a comprehensive experiment featuring three distinct datasets from the same simulated environment to evaluate the impact of texture variations on model performance. Our approach introduces a combination of aging texture layers to six industrial assets for object detection. To assess performance, we trained three Deep Learning architectures with these datasets and evaluated them by inferring on real images captured in industrial settings. The results of our experiments demonstrate the effectiveness of the aging textures in real-world scenarios, affirming the success of our proposed approach in enhancing object detection models for industrial applications.