MDWD：用于密集城市环境中城市生活垃圾检测的街景级数据集
MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments
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中文总结 AI 辅助
本文提出MDWD街景级生活垃圾检测基准数据集，涵盖5类生活垃圾，通过YOLO系列及Transformer检测器测试，RF-DETR-M性能最优，为城市垃圾视觉监测研究提供基准。
中文摘要 AI 辅助
城市环境的自动化视觉监测是计算机视觉领域不断发展的研究方向，但城市生活垃圾检测在专用基准资源中仍未得到充分体现。现有垃圾相关数据集主要针对单个垃圾检测、航拍图像或图像级分类，均未同时提供街景图像、实例级定位以及结构化市政收集场景下的生活垃圾类别划分。本文提出马耳他生活垃圾数据集（Maltese Domestic Waste Dataset，MDWD），这是一个街景级基准，包含3697张高分辨率图像，以及在马耳他市政收集系统所代表的5类生活垃圾中人工标注的11461个实例。该数据集涵盖了位置、光照、物体尺度、遮挡和城市环境的显著变化。为建立可复现的基准，对多代YOLO系列模型及基于Transformer的检测器进行了跨架构基准测试。在测试集上，RF-DETR-M实现了最强的整体性能，mAP50达94.49%，F1分数为93.56%；而容量更小的变体在参数数量大幅减少的情况下仍保持了有竞争力的精度。这些结果表明，MDWD可支持紧凑型实时检测器和基于Transformer的模型的有效训练，为基于视觉的城市垃圾监测领域的未来研究建立了基准。
英文摘要
Automated visual monitoring of urban environments is a growing Computer Vision research area, but municipal solid waste detection remains under-represented in dedicated benchmark resources. Existing waste-related datasets predominantly address individual litter detection, aerial imagery, or image-level classification, and none simultaneously provide street-level imagery, instance-level localization, and categorization of domestic waste streams within a structured municipal collection context. This paper introduces the Maltese Domestic Waste Dataset (MDWD), a street-level benchmark comprising 3,697 high-resolution images and 11,461 manually annotated instances across five domestic waste categories representative of Malta's municipal collection system. The dataset captures substantial variation in location, illumination, object scale, occlusion, and urban context. To establish reproducible baselines, a cross-architecture benchmark is conducted across multiple generations of the YOLO family and a transformer-based detector. On the test set, RF-DETR-M achieves the strongest overall performance with an mAP50 of 94.49% and an F1-score of 93.56%, whilst smaller-capacity variants maintain competitive accuracy at substantially reduced parameter counts. These results indicate that MDWD supports effective training across both compact real-time detectors and transformer-based models, establishing a benchmark for future research in vision-based municipal waste monitoring.
发表机构
- University of Malta（马耳他大学）
- DAWL AI Lab（DAWL人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。
