濒危警报：一种经过实地验证的自训练方案，用于检测和保护道路及路边的受威胁野生动物
Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides
AI总结：
针对道路交通事故威胁稀有动物生存的问题，本文提出一种结合云边计算与自动标注的自训练方法，并引入LA-NMS机制，经五个月实地部署验证，有效提升了目标检测精度与置信度。
摘要：
交通事故是一个全球性的安全问题，每年导致大量人员死亡。其中相当一部分死亡是由动物与车辆碰撞（AVCs）造成的，这不仅危及人类生命，还对动物种群构成严重威胁。本文提出了一种创新的自训练方法，旨在检测稀有动物，例如澳大利亚的食火鸡，其生存正受到道路事故的威胁。所提出的方法解决了关键的现实世界挑战，包括在资源有限的环境中获取和标注稀有动物物种的传感器数据。它通过利用云和边缘计算以及自动数据标注，迭代地提高实地部署模型的检测性能来实现这一点。我们的方法引入了标签增强非极大值抑制（LA-NMS），该方法结合了视觉语言模型（VLM）以实现自动化数据标注。在为期五个月的部署中，我们确认了该方法的稳健性和有效性，从而提高了目标检测精度并增加了预测置信度。源代码可在以下网址获取：https://github.com/acfr/CassDetect
英文摘要：
Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), which not only endanger human lives but also present serious risks to animal populations. This paper presents an innovative self-training methodology aimed at detecting rare animals, such as the cassowary in Australia, whose survival is threatened by road accidents. The proposed method addresses critical real-world challenges, including acquiring and labelling sensor data for rare animal species in resource-limited environments. It achieves this by leveraging cloud and edge computing, and automatic data labelling to improve the detection performance of the field-deployed model iteratively. Our approach introduces Label-Augmentation Non-Maximum Suppression (LA-NMS), which incorporates a vision-language model (VLM) to enable automated data labelling. During a five-month deployment, we confirmed the method's robustness and effectiveness, resulting in improved object detection accuracy and increased prediction confidence. The source code is available: https://github.com/acfr/CassDetect
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
