Intelligent Bear Prevention System Based on Computer Vision: An Approach to Reduce Human-Bear Conflicts in the Tibetan Plateau Area, China

• URL: http://arxiv.org/abs/2503.23178v1
• Date: Sat, 29 Mar 2025 18:10:11 GMT
• Title: Intelligent Bear Prevention System Based on Computer Vision: An Approach to Reduce Human-Bear Conflicts in the Tibetan Plateau Area, China
• Authors: Pengyu Chen, Teng Fei, Yunyan Du, Jiawei Yi, Yi Li, John A. Kupfer,
• Abstract summary: Conflicts between humans and bears on the Tibetan Plateau present substantial threats to local communities.<n>This research introduces a novel strategy that incorporates computer vision alongside Internet of Things (IoT) technologies to alleviate these issues.
• Score: 7.9835451566800195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD) [0.0]
  Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity.<n>Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments.<n>This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence.
  arXiv  Detail & Related papers  (2025-05-16T00:59:31Z)
• Inteligencia Artificial para la conservación y uso sostenible de la biodiversidad, una visión desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia) [0.0]
  This document aims to analyze the scope of this research area from a perspective focused on Colombia and the Neotropics.<n>We present current uses such as automatic species identification from images and recordings, species modeling, and in silico bioprospecting.<n>It also seeks to open a space for dialogue on the development of policies that promote the responsible and ethical adoption of AI in local contexts.
  arXiv  Detail & Related papers  (2025-03-17T16:47:05Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO [0.0]
  This work introduces a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images.<n>Deal-YOLO employs multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box.<n>The model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-03-06T18:46:10Z)
• Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach [84.20358039333756]
  We introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions.<n>We minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers.<n>We propose a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead.
  arXiv  Detail & Related papers  (2025-03-02T04:02:58Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  We introduce a novel framework to detect instability in smart grids by employing only stable data.<n>It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator.<n>Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5% in predicting grid stability and up to 98.9% in detecting adversarial attacks.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation [0.0]
  The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management.<n>This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation.
  arXiv  Detail & Related papers  (2024-12-28T03:18:56Z)
• AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10 [0.07255608805275862]
  This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata) A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections.
  arXiv  Detail & Related papers  (2024-11-22T10:36:29Z)
• Harnessing Artificial Intelligence for Wildlife Conservation [0.0937465283958018]
  Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes this data with convolutional neural networks (CNNs) and Transformer architectures to monitor species. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention.
  arXiv  Detail & Related papers  (2024-08-30T09:13:31Z)
• The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition [136.32656319458158]
  The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries. The competition culminated in 15 top-performing solutions.
  arXiv  Detail & Related papers  (2024-05-14T17:59:57Z)
• Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy [65.77763092833348]
  This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse.<n>We take into account user intent, the specific scientific domain, and their potential impact on the external environment.<n>We propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles [73.15950858151594]
  This paper presents Latent Optimistic Value Exploration (LOVE), a strategy that enables deep exploration through optimism in the face of uncertain long-term rewards. We combine latent world models with value function estimation to predict infinite-horizon returns and recover associated uncertainty via ensembling. We apply LOVE to visual robot control tasks in continuous action spaces and demonstrate on average more than 20% improved sample efficiency in comparison to state-of-the-art and other exploration objectives.
  arXiv  Detail & Related papers  (2020-10-27T22:06:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.