{"ID":2823502,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00398","arxiv_id":"2601.00398","title":"RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection","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 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.","short_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 ref...","url_abs":"https://arxiv.org/abs/2601.00398","url_pdf":"https://arxiv.org/pdf/2601.00398v1","authors":"[\"Tao Wu\",\"Qing Xu\",\"Xiangjian He\",\"Oakleigh Weekes\",\"James Brown\",\"Wenting Duan\"]","published":"2026-01-01T17:22:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":605501,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2823502,"paper_url":"https://arxiv.org/abs/2601.00398","paper_title":"RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection","repo_url":"https://github.com/xq141839/RoLID-11K","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}