首个真实低清车牌识别竞赛，99支队伍参赛，冠军准确率达82.13%。
ICPR 2026 Competition on Low-Resolution License Plate Recognition
- 基于2万条真实低清车牌数据，构建竞赛数据集
- 冠军团队识别率82.13%，四队超80%
- 展示深度学习在低质图像识别中的最新进展
低分辨率车牌识别（LRLPR）在实际监控场景中仍具挑战性，因远距离拍摄、压缩伪影及恶劣成像条件导致车牌可读性严重下降。为推动该领域发展，我们组织了ICPR 2026低分辨率车牌识别竞赛，这是首个专注于使用真实操作条件下采集的低质量数据的专项竞赛。竞赛基于LRLPR-26数据集，包含20,000个训练轨迹和3,000个测试轨迹，每个训练轨迹含5张低分辨率与5张高分辨率同车牌图像。共有来自41个国家的269支队伍注册，99支队伍提交有效结果。冠军团队识别率达82.13%，另有四队突破80%。本文介绍竞赛设计、评估流程与主要成果，并总结前五名团队方法，探讨当前趋势与未来研究方向。竞赛主页：https://icpr26lrlpr.github.io/
原文摘要 · Abstract (English)
Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and adverse imaging conditions can severely degrade license plate legibility. To promote progress in this area, we organized the ICPR 2026 Competition on Low-Resolution License Plate Recognition, the first competition specifically dedicated to LRLPR using real low-quality data collected under operationally relevant conditions. The competition was based on the LRLPR-26 dataset, which comprises 20,000 training tracks and 3,000 test tracks; each training track contains five low-resolution and five high-resolution images of the same license plate. Notably, a total of 269 teams from 41 countries registered for the competition, and 99 teams submitted valid entries in the Blind Test Phase. The winning team achieved a Recognition Rate of 82.13%, and four teams surpassed the 80% mark, highlighting both the high level of competition at the top of the leaderboard and the continued difficulty of the task. In addition to presenting the competition design, evaluation protocol, and main results, this paper summarizes the methods adopted by the top-5 teams and discusses current trends and promising directions for future research on LRLPR. The competition webpage is available at https://icpr26lrlpr.github.io/
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