Deep Generative Adversarial Networks for Dynamic Texture Analysis and Real-Time Slip Prediction in Industrial Flooring

**Abstract:** This research introduces a novel approach to slip detection and prevention in industrial settings utilizing Deep Generative Adversarial Networks (DGANs) for dynamic texture analysis and real-time slip prediction. Our method leverages a modified DGAN architecture to learn and model the complex, time-varying textural properties of industrial flooring under various environmental conditions (moisture, debris, wear). The system autonomously adapts to changes, providing accurate real-time slip risk assessment, far surpassing the limitations of traditional threshold-based systems. This technology can be immediately commercialized for industrial safety applications, leading to a significant reduction in workplace accidents and associated costs.