Intelligent Bear Prevention System Based on Computer Vision: An Approach to Reduce Human-Bear Conflicts in the Tibetan Plateau Area, China
원문 초록
Conflicts between humans and bears on the Tibetan Plateau present substantial threats to local communities and hinder wildlife preservation initiatives. This research introduces a novel strategy that incorporates computer vision alongside Internet of Things (IoT) technologies to alleviate these issues. Tailored specifically for the harsh environment of the Tibetan Plateau, the approach utilizes the K210 development board paired with the YOLO object detection framework along with a tailored bear-deterrent mechanism, offering minimal energy usage and real-time efficiency in bear identification and deterrence. The model's performance was evaluated experimentally, achieving a mean Average Precision (mAP) of 91.4%, demonstrating excellent precision and dependability. By integrating energy-efficient components, the proposed system effectively surpasses the challenges of remote and off-grid environments, ensuring uninterrupted operation in secluded locations. This study provides a viable, eco-friendly, and expandable solution to mitigate human-bear conflicts, thereby improving human safety and promoting bear conservation in isolated areas like Yushu, China.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.