AI 中文总结
介绍用于极端野外车牌识别的参赛系统，结合HAT超分辨率前端、两个场景文本识别器及投票方案，将XLPSR视为图像清晰度控制的识别任务，运行速度快，在ICIP 2026挑战公开验证排行榜获9.73 wECR。
AI 中文摘要
我们介绍了参加ICIP 2026极端野外车牌超分辨率（XLPSR）大挑战的参赛作品，其在公开验证排行榜上的加权等效字符识别率（wECR）为9.73。该系统将混合注意力Transformer超分辨率（HAT）前端与两个场景文本识别器（PARSeq-S和CLIP4STR-B）的集成以及对不确定位置弃权的置信度加权字符投票方案相结合。我们将XLPSR视为由图像清晰度控制的识别任务，通过弃权明确利用不对称评分规则（+2 / -1 / 0）。我们的管道在RTX 3090上每个序列运行1.7秒（最大2.7秒，p99为2.4秒），远低于60秒/序列的Docker预算。
英文摘要
We describe our entry to the ICIP 2026 Grand Challenge on Extreme In-the-Wild License Plate Super-Resolution (XLPSR), which scored 9.73 wECR on the public validation leaderboard. The system pairs a Hybrid Attention Transformer super-resolution (HAT) front-end with an ensemble of two scene-text recognisers (PARSeq-S and CLIP4STR-B) and a confidence-weighted character-voting scheme that abstains on uncertain positions. We treat XLPSR as a recognition task gated by image legibility: the SR step exists to lift characters out of sub-pixel territory, and the asymmetric scoring rule (+2 / -1 / 0) is exploited explicitly through abstention. Our pipeline runs in 1.7 s per sequence on RTX 3090 (max 2.7 s, p99 2.4 s), well under the 60 s/sequence Docker budget.
Comments2 pages, 1 figure, 1 table. Accepted at the IEEE ICIP 2026 Grand Challenge on Extreme In-the-Wild License Plate Super-Resolution (XLPSR). Top-8 finalist
