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.本室内目标检测（object detection）数据集分为训练划分集（占比94%）、验证划分集（占比4%）与测试划分集（占比2%），其中训练划分集包含12012张图像，验证划分集490张，测试划分集245张，全数据集共计12747张图像。该数据集共涵盖7个类别，分别为灭火器（fire extinguisher）、货架（shelf）、门（door）、桌子（table）、行人（human）、椅子（chair）与垃圾桶（bin）。全数据集的类别分布如下：灭火器2693个实例，货架1384个，门8530个，桌子3959个，行人5010个，椅子9687个，垃圾桶3475个。在训练划分集中，椅子类别占比最高，共计9090个实例，其次依次为门（8050个）、行人（4711个）、桌子（3731个）、垃圾桶（3275个）、灭火器（2547个）与货架（1316个）。验证划分集的类别分布呈现相似的层级结构，同样以椅子类（429个）居首，随后依次为门（305个）、行人（216个）、桌子（161个）、垃圾桶（135个）、灭火器（97个）与货架（48个）。测试划分集中，门以175个实例略微领先，紧随其后的是椅子（168个），随后依次为行人（83个）、桌子（67个）、垃圾桶（65个）、灭火器（49个）与货架（20个）。数据集预处理步骤包含自动图像方向校正与将所有图像统一缩放至640×640分辨率。为提升模型在实际应用中的泛化能力，我们采用了多种数据增强（data augmentation）技术，包括水平与垂直翻转、90度旋转（顺时针、逆时针及上下翻转）、-15°至+15°的随机旋转、水平与垂直方向±10°的剪切变换，以及亮度调整幅度为-15%至+15%的亮度校正。此外，经过标注、预处理与数据增强后的本数据集，可有效提升室内场景下的目标检测精度。