Deep Learning-Based System for Detection and Classification of Household Entry Points Using YOLOv11

Object state detection in home environments presents a crucial challenge for smart home systems and robotics applications. This paper presents deep learning approach for detecting households' entry points - doors and windows. It uses the You Only Look Once (YOLO) v11 object detection model. Custom dataset comprising images of doors and windows in various lighting conditions and angles was constructed for this purpose. Using the Roboflow platform, images were annotated with bounding boxes and preprocessed for standardization, with data augmentation applied to increase sample diversity. The resulting dataset was then divided into training, validation, and testing sets for model development and evaluation. The trained model was deployed through Roboflow's API, enabling seamless integration with Python through the dedicated library, which allowed for efficient real-time inference in our application. Experimental results indicate that the proposed approach offers a reliable solution for automated households entry point detection, with potential applications in smart home systems, security monitoring, and robotic navigation. The implementation demonstrates robust performance across varying lighting conditions and viewing angles, making it suitable for real-world applications. This research contributes to the growing field of automated home monitoring systems by providing a practical solution for entryway state detection using contemporary deep learning techniques.