Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring

Remote cameras (?trail cameras?) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML model?s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance).

远程相机（亦称野外触发相机，trail cameras）是一类用于非侵入式、持续性野生动物监测的主流工具。随着其在野生动物研究领域的应用愈发广泛，机器学习（Machine Learning，ML）技术正逐步被用于自动化或加速耗时费力的图像标注（即标记）流程。已有研究证实，人机混合标注方法可大幅提升标注效率（即单张图像的标注耗时）。但此类效率提升的潜力，核心取决于机器学习模型预测结果的正误比例。本研究采用一款可在野外相机影像中为动物、人类及车辆生成边界框的机器学习模型（MegaDetector）开展实验，以探究机器学习模型的性能对其辅助加速人工标注能力的影响。本研究共招募六名参与者，采用三种标注方法对采集自美国佛蒙特州与缅因州12个监测点、采集时段为2022年1月至9月的野外相机图像进行标注；其中一种方法配有机器学习边界框辅助标注，其余两种方法无辅助标注。