A synthetic outdoor waste image dataset with YOLO-format annotations for object detection

This dataset contains 6,000 synthetic RGB images for outdoor waste object detection, annotated in YOLO format. The images represent realistic outdoor environments such as streets, parks, roadsides, and public open spaces, designed to support research in environmental monitoring and intelligent waste management. The dataset includes 10 waste categories: plastic, paper, cardboard, metal, glass, organic waste, battery waste, e-waste, cloth, and other waste. All images are resized to a fixed resolution of 640 × 640 pixels and are accompanied by normalized YOLO annotation files (class_id, x_center, y_center, width, height). In total, the dataset contains 21,057 annotated bounding boxes, with an average of 3.51 objects per image.

The images were generated using a synthetic data generation pipeline, where annotated waste object instances were cropped from labeled source images and composited onto real outdoor background scenes sampled from the COCO dataset. To improve realism and diversity, object placement, scale, orientation, and photometric properties were randomly varied, while quality control checks ensured annotation correctness and realistic object visibility. This dataset is intended for training, validation, and benchmarking of object detection models, particularly for challenging outdoor waste detection scenarios involving varying object scales, cluttered backgrounds, and class diversity.