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.

翻译：路边垃圾带来了环境、安全和经济方面的挑战，然而当前的监测依赖于劳动密集型调查和公众报告，空间覆盖范围有限。现有的垃圾检测视觉数据集主要关注街道水平的静态图像、航拍场景或水生环境，未能反映行车记录仪视频的独特特性——在行车记录仪画面中，垃圾目标极其微小、稀疏且嵌入在杂乱的路边背景中。我们提出了RoLID-11K，这是首个基于行车记录仪的大规模路边垃圾检测数据集，包含超过1.1万张标注图像，涵盖了英国多样的驾驶条件，并呈现出显著的长尾分布和小目标分布特征。我们对广泛的现代检测器进行了基准测试，从注重精度的Transformer架构到实时的YOLO模型，并分析了它们在这一挑战性任务上的优势与局限。我们的结果表明，尽管CO-DETR及相关Transformer模型取得了最佳的定位精度，但实时模型仍受限于粗糙的特征层次结构。RoLID-11K为动态驾驶场景下的极端小目标检测建立了一个具有挑战性的基准，旨在支持开发可扩展、低成本的路边垃圾监测系统。该数据集可通过 https://github.com/xq141839/RoLID-11K 获取。