Work

Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides

Year: 2025

Type: article

Abstract: Traffic accidents, including animal-vehicle collisions (AVCs), endanger both humans and wildlife. This letter presents an innovative self-training methodology aimed at detecting rare animals, such as cassowaries in Australia, whose survival is threatened by road accidents. The proposed method addresses critical real-world challenges, including the acquisition and labelling of 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 robustness and effectiveness of the method, achieving improved object detection accuracy and increased prediction confidence.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3604697

Authors: Kunming Li, Mao Shan, Stephany Berrio Perez, Katie Luo, Stewart Worrall

Institutions: The University of Sydney, Cornell University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Life on land

Open Access status: green

Funders: Australian Government, Cooperative Research Centres, Australian Government Department of Industry, Queensland Government, Department of Transport and Main Roads, Queensland Government