用AI自动识别野生黑猩猩麻风病，提升野生动物疾病监测效率
Automating Visual Recognition of Leprosy in Wild Chimpanzees
- 构建首个野生动物麻风病检测深度学习流水线
- 在303段视频中准确识别出12.5万张带标注的病灶图像
- 静态皮肤症状更适合逐帧分析，而非复杂视频模型
麻风病（Mycobacterium leprae）已在西非野生西部黑猩猩（Pan troglodytes verus）中确诊，表现为清晰且渐进性的视觉症状。在景观尺度上人工审查相机陷阱视频不现实，亟需自动化筛查。本文提出首个针对野生动物麻风病检测的深度学习流程，并贡献了包含125,670个标注边界框图像的PanLep300数据集，覆盖953条轨迹、303段相机陷阱视频，采用生态动机划分策略，保留完整个体与摄像机安装位置。我们对比了二维（2D）、时间聚合（2.5D）和视频级（3D）分类方法，发现简单地对图像块预测结果进行聚合，始终达到或优于学习型时序模型与端到端视频架构——这与麻风病静态皮肤表现一致。进一步发现，当轨迹片段包含部分可见个体时（常出现在轨迹起止处），性能下降，但可通过针对性构建与聚合策略解决。
原文摘要 · Abstract (English)
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.
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