Back Home：面向生态修复的海贝壳识别计算机视觉解决方案
Back Home: A Computer Vision Solution to Seashell Identification for Ecological Restoration
AI总结：
针对哥斯达黎加查获海贝壳来源难核实、无法回归原生海岸的问题，研究构建了标注海岸标签的BackHome19K大规模图像语料库，提出带异常过滤的轻量级实时推断流水线，部署为Web应用后高效助力海贝壳生态修复。
摘要：
非法纪念品收集每年从哥斯达黎加海滩掠走约5吨海贝壳。然而，这些标本被查获后，由于缺乏相关信息，其海岸来源——太平洋还是加勒比海——难以核实，导致当地当局没收后无法将其送回原生环境。为解决这一问题，我们构建了BackHome19K，这是首个标注有海岸级别标签的大规模图像语料库（包含19058张照片、516个物种），并提出了一种轻量级流水线，可在移动端CPU上实时推断来源。经过训练的异常过滤器对上传图像进行预筛选，提升了对用户生成噪声的鲁棒性。在留出测试集上，分类器达到86.3%的平衡准确率，而过滤器在零假阴性的情况下，拒绝了180个域外对象中的93%。该系统以Web应用形式部署，已为野生动物管理人员处理了70000枚贝壳，单张图像处理耗时不到3秒，使没收的标本能够安全返回其原生生态系统。数据集可在https://huggingface.co/datasets/FIFCO/BackHome19K获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
