Indoor Object Detection Dataset

The indoor object detection dataset is divided into three parts: the training set (94%), validation set (4%), and test set (2%), with 12,012 images for training, 490 for validation, and 245 for testing. The dataset contains a total of 12,747 images. The dataset is organized into seven classes, which are fire extinguisher, shelf, door, table, human, chair, and bin. The dataset class distribution for 7 classes: 2,693 fire extinguishers, 1,384 shelves, 8,530 doors, 3,959 tables, 5,010 humans, 9,687 chairs, and 3,475 bins. Moreover, in the training split, the chair class is most prevalent with 9,090 instances, followed by doors (8,050), humans (4,711), tables (3,731), bins (3,275), fire‑extinguishers (2,547) and shelves (1,316). The validation set shows a similar hierarchy—chairs (429) leading, then doors (305), humans (216), tables (161), bins (135), fire‑extinguishers (97) and shelves (48). In the test split, doors edge ahead with 175 instances, while chairs follow closely at 168, then humans (83), tables (67), bins (65), fire‑extinguishers (49) and shelves (20). In addition, preprocessing steps included auto-orientation and resizing all images to 640×640. To improve generalization for real-world applications, we applied data augmentation techniques, including horizontal and vertical flipping, 90-degree rotations (clockwise, counterclockwise, and upside down), random rotations within -15° to +15°, shearing within ±10° horizontally and vertically, and brightness adjustments between -15% and +15%. Additionally, this annotated, preprocessed, and augmented dataset enhances object detection accuracy in indoor scenes.