用无人机航拍检测小动物驼鹿，提升高效精准的野外追踪能力
Tracking Moose using Aerial Object Detection
- 通过图像拼接增强数据，优化小目标检测模型在有限算力下的表现
- 三种检测模型在最佳配置下均达93%以上mAP@IoU=0.5，精度达标
- 轻量模型性能接近复杂模型，适合资源受限的无人机部署
航空野生动物追踪对保护工作至关重要，需在机载影像中检测地面上微小的目标。传统载人飞机成本高、风险大且扰动环境；自主无人机则受计算能力限制，难以运行复杂的AI系统。由于目标可能仅占几像素，小物体检测面临巨大挑战，同时需兼顾计算效率。本文采用图像拼接增强策略，研究不同设置下模型性能表现。对比分析了三种架构各异的主流目标检测器，调整拼接方法超参数并评估检测准确率。每种模型在至少一种拼接配置下均达到93%以上mAP@IoU=0.5。统计分析深入揭示了各类因素影响。结果还表明，更快更简单的模型在此任务中表现与高算力模型相当，在小尺度拼接条件下仍具优异性能，支持无人机实际部署。数据集与模型将公开于https://github.com/chrisindris/Moose。
原文摘要 · Abstract (English)
Aerial wildlife tracking is critical for conservation efforts and relies on detecting small objects on the ground below the aircraft. It presents technical challenges: crewed aircraft are expensive, risky and disruptive; autonomous drones have limited computational capacity for onboard AI systems. Since the objects of interest may appear only a few pixels wide, small object detection is an inherently challenging computer vision subfield compounded by computational efficiency needs. This paper applies a patching augmentation to datasets to study model performance under various settings. A comparative study of three common yet architecturally diverse object detectors is conducted using the data, varying the patching method's hyperparameters against detection accuracy. Each model achieved at least 93\% mAP@IoU=0.5 on at least one patching configuration. Statistical analyses provide an in-depth commentary on the effects of various factors. Analysis also shows that faster, simpler models are about as effective as models that require more computational power for this task and perform well given limited patch scales, encouraging UAV deployment. Datasets and models will be made available via https://github.com/chrisindris/Moose.
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