Scholarlink

<p>Infrared Camera Traps (ICTs) are widely used in ecological research as a noninvasive wildlife monitoring technique, particularly for the detection and identification of animal targets. Existing ICT data screening methods face challenges in recognizing animals against complex backgrounds, particularly fast-moving or small targets. To address these issues, we proposed a target-oriented enhanced data-screening method called GFD-YOLO, which emphasized key locations in images to effectively guide the focus of the model toward target regions, thereby improving detection accuracy. We compared the effects of different preprocessing methods on detection performance. Results revealed that the proposed method improved the mean Average Precision (mAP) by 16.96%, precision by 10.13%, and recall by 24.85% compared to the YOLOv11n model. Therefore, the preprocessing method proposed in this study had significant advantages in reducing false negatives and false positives and was adaptable to wildlife detection tasks under different background conditions. In addition, this method demonstrated higher robustness in scenarios involving lighting variations and fast-moving targets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel target-oriented enhanced infrared camera trap data screening method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月10日

查看全文
  • Yinfan Cai,
  • Kaikai Tian,
  • Liling Ji,
  • Yongxuan Xiao,
  • Dapeng Pang,
  • Pengfei Hou,
  • YunFeng Ji,
  • Lanrong Wang,
  • Xiaonan Li,
  • Jingru Lu,
  • Wenwen Zhang,
  • Jun Wu,
  • Peng Cui,
  • Baowei Zhang

摘要

Infrared Camera Traps (ICTs) are widely used in ecological research as a noninvasive wildlife monitoring technique, particularly for the detection and identification of animal targets. Existing ICT data screening methods face challenges in recognizing animals against complex backgrounds, particularly fast-moving or small targets. To address these issues, we proposed a target-oriented enhanced data-screening method called GFD-YOLO, which emphasized key locations in images to effectively guide the focus of the model toward target regions, thereby improving detection accuracy. We compared the effects of different preprocessing methods on detection performance. Results revealed that the proposed method improved the mean Average Precision (mAP) by 16.96%, precision by 10.13%, and recall by 24.85% compared to the YOLOv11n model. Therefore, the preprocessing method proposed in this study had significant advantages in reducing false negatives and false positives and was adaptable to wildlife detection tasks under different background conditions. In addition, this method demonstrated higher robustness in scenarios involving lighting variations and fast-moving targets.