As human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large-scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open-source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open-source object detection model MegaDetector in cross-regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long-term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross-regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long-term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.

中文翻译：

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通过相机陷阱和机器学习自动监控访客和野生动物

随着人类在自然区域的活动增加，了解人类与野生动物的相互作用至关重要。大数据方法，如大规模相机陷阱研究，对于研究这些相互作用变得越来越重要。此外，开源物体检测模型正在迅速改进，并且在增强来自人类和野生动物活动的相机陷阱数据的图像处理方面具有巨大潜力。在本研究中，我们评估了开源目标检测模型 MegaDetector 在使用相机陷阱进行跨区域监控中的性能。通过将检测结果与来自三个研究区域的超过 300 000 张相机陷阱图像的手动分类进行比较，评估检测和计数人类、动物和车辆的性能。而且，我们研究错误分类的结构模式，并评估生态研究中进行的典型时间分析的检测模型的结果。总体而言，检测模型的准确率非常高，动物准确率为96.0%，人为93.8%，车辆为99.3%。结果揭示了错误分类的系统模式，可以自动识别和删除。此外，我们还表明，检测模型可以很容易地用于对图像上的人和动物进行计数，每张图像的人数低估为-0.05，车辆数低估为-0.01，动物数低估为-0.01。最重要的是，手动分类的人类和野生动物活动的长期时间序列中的时间模式与检测模型的分类结果高度相关（Pearson 的r  = 0.996，p  < 0.001）和每日核活动密度对于手动和自动分类几乎相同。结果证明了检测模型在跨区域相机陷阱研究的图像分类过程中的整体适用性，无需进一步的人工干预。除了处理速度大大加快外，该模型还适合长期监测，并在遵守隐私法规的同时允许科学研究的可重复性。