RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection
Tao Wu, Qing Xu, Xiangjian He, Oakleigh Weekes, James Brown, Wenting Duan

TL;DR
RoLID-11K introduces a large-scale dashcam dataset for small-object roadside litter detection, enabling the development of scalable monitoring systems and benchmarking modern detectors in challenging driving scenarios.
Contribution
The paper presents RoLID-11K, the first extensive dataset for small-object litter detection from dashcam footage, and benchmarks various detection models on this new challenging dataset.
Findings
Transformers like CO-DETR achieve high localization accuracy.
Real-time models are limited by coarse feature hierarchies.
RoLID-11K provides a challenging benchmark for small-object detection in driving scenes.
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Microplastics and Plastic Pollution · Remote Sensing and LiDAR Applications
