用图像识别帮被没收的贝壳找回家，准确率达86.3%。
Back Home: A Computer Vision Solution to Seashell Identification for Ecological Restoration
- 构建19,058张贝壳图集，标注海岸归属信息
- 轻量模型实现在手机端3秒内完成识别，准确率86.3%
- 可过滤93%异常样本，适合执法与生态修复场景
非法纪念品采集每年从哥斯达黎加海滩带走约五吨贝壳。然而，一旦这些标本被查获，因缺乏来源信息，难以确认其来自太平洋或加勒比海，导致无法归还。为此，我们提出BackHome19K，首个大规模图像语料库（19,058张照片，516个物种），并带有海岸级标签；同时设计轻量级流程，可在移动端CPU上实时推断产地。训练后的异常过滤器可有效应对用户上传噪声，提升鲁棒性。在独立测试集上，分类器达到86.3%的平衡准确率，过滤器在180个域外对象中拒绝93%且零误报。系统已部署为网页应用，处理7万枚贝壳仅需三秒/张，助力执法部门将查获标本安全送回原生生态系统。数据集已开放：https://huggingface.co/datasets/FIFCO/BackHome19K
原文摘要 · Abstract (English)
Illegal souvenir collection strips an estimated five tonnes of seashells from Costa Rica's beaches each year. Yet, once these specimens are seized, their coastal origin -- Pacific or Caribbean -- cannot be verified easily due to the lack of information, preventing their return when confiscated by local authorities. To solve this issue, we introduce BackHome19K, the first large-scale image corpus (19,058 photographs, 516 species) annotated with coast-level labels, and propose a lightweight pipeline that infers provenance in real time on a mobile-grade CPU. A trained anomaly filter pre-screens uploads, increasing robustness to user-generated noise. On a held-out test set, the classifier attains 86.3% balanced accuracy, while the filter rejects 93% of 180 out-of-domain objects with zero false negatives. Deployed as a web application, the system has already processed 70,000 shells for wildlife officers in under three seconds per image, enabling confiscated specimens to be safely repatriated to their native ecosystems. The dataset is available at https://huggingface.co/datasets/FIFCO/BackHome19K
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