Stray Dogs Tracking Based on Drone and AI
Hsien-Huang P. Wu, Hao-Nien Wei, Hsing-Chieh Wang · 2024
This study is dedicated to evaluate the effectiveness of utilizing artificial intelligence (AI) and drone technology for the detection and management of stray dog issues worldwide. By combining the aerial photography advantages of drones with deep learning techniques, the aim is to enhance the accuracy and efficiency of estimating stray dog populations and their locations. This provides animal management departments with a highly efficient tool for monitoring and quantity control. Through the collection of extensive aerial image data and the use of deep learning algorithms, such as YOLO, for the training of AI models, we optimize the identification and tracking capabilities for stray dogs. This study also leverages the flight data and real-time streaming images returned by drones to provide the detection system with immediate and accurate positioning capabilities. The results demonstrate that integrating AI and drone technology in the management and control of stray dogs can improve the accuracy of detection and tracking. It also enhances the efficiency of animal management tasks and the protection of public safety and animal welfare.