用自训练方法提升稀有动物道路检测，降低车撞动物事故风险
Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides
- 结合云边协同与视觉语言模型自动标注，迭代优化检测模型
- 五个月实地部署后检测准确率提升，预测置信度显著增强
- 适合野生动物保护、智能交通系统研究者使用
交通事故是全球性的安全问题，每年造成大量伤亡。其中不少死亡源于动物-车辆碰撞（AVCs），不仅威胁人类生命，也对动物种群构成严重威胁。本文提出一种创新的自训练方法，用于检测濒危动物如澳大利亚的双领鸻，其生存正受道路事故威胁。该方法在资源受限环境下，解决罕见动物物种传感器数据获取与标注难题，通过融合云端与边缘计算，以及自动数据标注技术，实现现场部署模型的持续性能提升。提出标签增强非极大值抑制（LA-NMS）策略，利用视觉语言模型（VLM）实现自动化数据标注。经过为期五个月的实地部署验证，方法展现出良好鲁棒性与有效性，检测准确率提高，预测置信度增强。源代码已公开：https://github.com/acfr/CassDetect
原文摘要 · Abstract (English)
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
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