SyntheticIndoorObjectDetectionDataset

The dataset was collected from the MyNursingHome dataset, available at https://data.mendeley.com/datasets/fpctx3svzd/1 , and curated to develop a synthetic indoor object detection dataset for autonomous mobile robots, or robots, for supporting researchers in detecting and classifying objects for computer vision and pattern recognition. From the original dataset containing 25 object categories, we selected six key categories—basket bin (499 images), sofa (499 images), human (499 images), table (500 images), chair (496 images), and door (500 images). Initially, we collected a total of 2,993 images from these categories; however, during the annotation process using Roboflow, we rejected 1 sofa, 10 tables, 9 chairs, and 12 door images due to quality concerns, such as poor image resolution or difficulty in identifying the object, resulting in a final dataset of 2,961 images. To ensure an effective training pipeline, we divided the dataset into 70% training (2,073 images), 20% validation (591 images), and 10% test (297 images). Preprocessing steps included auto-orientation and resizing all images to 640×640 pixels to maintain uniformity. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counter-clockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. This augmentation process expanded the dataset to 7,107 images, with 6,219 images for training (88%), 597 for validation (8%), and 297 for testing (4%). Moreover, this well-annotated, preprocessed, and augmented dataset significantly improves object detection performance in indoor settings.