Object Recognition in Husehold Environments Using YOLOv11

Object detection has become a key component in many computer vision applications, particularly in areas such as smart homes, health care, robotics and assistive technologies. Detecting daily household items accurately in real-world environments remains challenging due to variations in object appearance, lighting conditions and background cluttering. In this study, we investigate the use of YOLOv11 for recognizing common household items such as keys, remote controllers, eye glasses and pills. A dataset consisting of the daily home items was collected and manually annotated using Roboflow in YOLO format. The dataset was divided into training, validation and testing sets, and preprocessing and data augmentation techniques were applied to improve model generalization. The YOLOv11 model was trained on the dataset using an RTX4060 GPU and evaluated using standard evaluation metrics such as precision, recall and mean Average Precision (mAP). Experimental results demonstrate that the trained model achieves strong detection performance across most classes, highlighting the effectiveness of YOLOv11 for custom, small-scale object detection tasks. The results indicate that the YOLOv11 can be effectively applied to real-world household object detection scenarios and provide a practical foundation for future smart home and automation applications.