RoLID-11K：一种用于小型 roadside 垃圾检测的 dashcam 数据集
RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection
AI总结：
RoLID-11K是一个大规模dashcam数据集，用于研究道路垃圾检测中的小物体识别问题，通过基准测试展示了不同检测模型的性能差异。
摘要：
道路垃圾在环境、安全和经济方面都带来了挑战，但当前的监控依赖于人力调查和公众报告，提供有限的空间覆盖。现有的垃圾检测视觉数据集主要关注街道级静态图像、空中场景或水下环境，而不反映dashcam视频的特殊特点，其中垃圾显得极其微小、稀疏且嵌入在杂乱的道路边缘背景中。我们引入了RoLID-11K，这是首个大规模的道路垃圾检测dashcam数据集，包含超过11000张标注图像，涵盖多样化的英国驾驶条件，并表现出显著的长尾分布和小物体分布。我们对一系列现代检测器进行了基准测试，从以精度为导向的Transformer架构到实时YOLO模型，并分析了这些模型在该挑战性任务中的优缺点。我们的结果表明，尽管CO-DETR和相关Transformer在定位精度上表现最佳，但实时模型仍受制于粗粒度的特征层次。RoLID-11K为动态驾驶场景中的极端小物体检测建立了具有挑战性的基准，并旨在支持开发可扩展、低成本的道路垃圾监控系统。该数据集可在https://github.com/xq141839/RoLID-11K获取。
英文摘要：
Roadside litter poses environmental, safety and economic challenges, yet current monitoring relies on labour-intensive surveys and public reporting, providing limited spatial coverage. Existing vision datasets for litter detection focus on street-level still images, aerial scenes or aquatic environments, and do not reflect the unique characteristics of dashcam footage, where litter appears extremely small, sparse and embedded in cluttered road-verge backgrounds. We introduce RoLID-11K, the first large-scale dataset for roadside litter detection from dashcams, comprising over 11k annotated images spanning diverse UK driving conditions and exhibiting pronounced long-tail and small-object distributions. We benchmark a broad spectrum of modern detectors, from accuracy-oriented transformer architectures to real-time YOLO models, and analyse their strengths and limitations on this challenging task. Our results show that while CO-DETR and related transformers achieve the best localisation accuracy, real-time models remain constrained by coarse feature hierarchies. RoLID-11K establishes a challenging benchmark for extreme small-object detection in dynamic driving scenes and aims to support the development of scalable, low-cost systems for roadside-litter monitoring. The dataset is available at https://github.com/xq141839/RoLID-11K.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
