dooridentification

Door identification, sometimes written as dooridentification, is the process of recognizing doors and their attributes in images, video, or other sensor data. It is used in robotics, autonomous navigation, building automation, and security to locate doors, determine their type, and infer relevant properties such as state (open or closed) and, when possible, dimensions or hardware configuration.

Techniques range from classical computer-vision methods to modern deep learning. Classical approaches use edge detection, texture, and shape cues for localization and simple classification, while contemporary systems rely on convolutional neural networks and object detectors to identify doors and classify types (hinged, sliding, pocket, revolving). Data modalities include RGB cameras, depth sensors, and LiDAR; multimodal fusion can improve robustness.

Applications include assistive navigation for visually impaired users, robot path planning and interaction, automatic inventory and asset management, and coordination of access-control systems. Challenges include occlusion by people or objects, varying lighting and reflections on glass, diverse door designs across regions, privacy considerations, and the need to operate in cluttered indoor environments.

Door identification intersects with broader fields such as object recognition, scene understanding, and human–robot interaction. It benefits from curated datasets and benchmarks focusing on architectural elements and door components, and from standards that define evaluation metrics for detection, classification, and state estimation.