用自适应角度间隔提升豹子个体识别准确率
Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
- 改进CosFace模型，引入自适应角度间隔增强特征区分度
- 在动态Top-5平均精度达0.8814，Top-5匹配率达0.9533
- 适合生物学家用于野生动物个体追踪与保护研究
基于相机陷阱图像的豹子个体精准识别对种群监测和生态研究至关重要。本文提出一种深度学习框架，通过独特斑点图案区分个体豹子。该方法采用改进的CosFace架构，引入新颖的自适应角度间隔机制，并设计融合RGB与边缘检测通道的预处理流程，突出模型关注的关键特征。实验表明，该方法显著优于三元组网络基线，在动态Top-5平均精度达0.8814，Top-5排名匹配检测达0.9533，展现出在开放集学习中的潜力。尽管未超越基于SIFT的Hotspotter算法，但为深度学习在有斑纹野生动物识别中的应用提供了重要进展。研究成果推动了计算机视觉发展，为生物学家研究与保护豹类种群提供有力工具，也为其他斑纹物种的捕获-再捕获研究奠定基础。
原文摘要 · Abstract (English)
Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. This approach employs a novel adaptive angular margin method in the form of a modified CosFace architecture. In addition, I propose a preprocessing pipeline that combines RGB channels with an edge detection channel to underscore the critical features learned by the model. This approach significantly outperforms the Triplet Network baseline, achieving a Dynamic Top-5 Average Precision of 0.8814 and a Top-5 Rank Match Detection of 0.9533, demonstrating its potential for open-set learning in wildlife identification. While not surpassing the performance of the SIFT-based Hotspotter algorithm, this method represents a substantial advancement in applying deep learning to patterned wildlife identification. This research contributes to the field of computer vision and provides a valuable tool for biologists aiming to study and protect leopard populations. It also serves as a stepping stone for applying the power of deep learning in Capture-Recapture studies for other patterned species.
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