发表机构
University of Exeter; University of Bristol; Wild Chimpanzee Foundation; Instituto da Biodiversidade e das Áreas Protegidas (IBAP)（埃克塞特大学; 布里斯托大学; 野生黑猩猩基金会; 生物多样性和保护区研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对野生黑猩猩麻风病视觉识别自动化问题，提出深度学习管道及数据集，通过基准测试不同分类方法，发现作物级预测简单聚合效果佳，且解决了部分可见个体帧抑制性能的问题。
AI 中文摘要
麻风病已在西非野生西部黑猩猩中得到证实，呈现出明显且渐进的视觉症状。在景观尺度上人工查看相机陷阱拍摄的画面不可行，因此需要自动化筛查。我们提出了首个用于野生动物麻风病检测的深度学习管道，并贡献了PanLep300数据集。我们对空间（2D）、时间聚合（2.5D）和基于视频（3D）的分类方法进行基准测试，发现作物级预测的简单聚合效果良好，还发现小轨迹中部分可见个体的帧会抑制性能，可通过针对性策略解决。
英文摘要
Leprosy (Mycobacterium leprae) has been confirmed in wild western chimpanzees (Pan troglodytes verus) in West Africa, presenting as clear and progressive visual symptoms. Manual review of camera-trap footage at landscape scale is infeasible, motivating the need for automated screening. We present the first deep learning pipeline for wildlife leprosy detection and contribute the PanLep300 dataset of 125,670 annotated bounding-box crops across 953 tracks from 303 camera-trap videos with ecologically-motivated splits that withhold whole individuals and camera installations. We benchmark spatial (2D), temporally aggregated (2.5D), and video-based (3D) classification approaches to investigate which approach is best suited to automated leprosy detection in wild apes. We find that simple aggregation of crop-level predictions consistently matches or outperforms both learned temporal models and end-to-end video architectures -- consistent with leprosy's static cutaneous presentation. We further find that performance is suppressed when tracklets contain frames of partially visible individuals -- as commonly occurs at the start and end of a track -- and demonstrate that this can be addressed through targeted construction and aggregation strategies.
