AUTOMATED DOOR STATE DETECTION USING DEEP LEARNING: A COMPUTER VISION APPROACH WITH ROBOFLOW PLATFORM

Abstract

This paper presents a deep learning-based approach for automated door state detection, capable of classifying doors as open, closed, or semi-open. The implementation leverages the Roboflow platform for comprehensive image processing and model development workflow. Our methodology encompasses data collection, annotation, augmentation, and model training using state-of-the-art deep learning architectures. The system demonstrates robust performance in real-world scenarios, offering potential applications in building automation, security systems, and smart home technologies. This work contributes to the growing field of automated building monitoring by providing a practical solution for door state recognition that can be integrated into existing surveillance and security infrastructures.