PlasticVision-BD: A Benchmark Dataset for Plastic Waste Detection

PlasticWaste-BD is a real-world annotated image dataset developed for automatic plastic waste detection and classification using computer vision and deep learning techniques. The dataset comprises high-quality images collected under diverse real-world environmental conditions, including variations in illumination, viewing angles, backgrounds, and object orientations, to reflect practical waste management scenarios. The dataset contains five common categories of plastic waste: Plastic-Bottle, Plastic-Cup, Plastic-Packet, Plastic-Straw, and Polythene. Every object instance has been manually annotated with bounding boxes following a rigorous annotation protocol to ensure accurate localization and consistent class labeling. Multiple rounds of quality verification were performed to minimize annotation errors and improve dataset reliability. PlasticWaste-BD is designed to support research in object detection, image classification, waste recognition, environmental monitoring, and intelligent recycling systems. The dataset is compatible with widely used deep learning frameworks, including YOLO, RT-DETR, Faster R-CNN, SSD, EfficientDet, and other modern computer vision models. It can also serve as a benchmark for evaluating novel detection algorithms and transfer learning approaches. The repository includes the complete annotated dataset, annotation files, dataset configuration files, metadata, and documentation necessary for reproducible research.