基于改进的YOLOX网络的野生动物目标检测
Bao XiaoAn1, Zhou LinQing2, Tu XiaoMei3
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China.
Scientific reports
|October 9, 2024
概括
这项研究引入了一种改进的YOLOX网络,用于在雨中夜间检测野生动物,大大提高了准确性并减少了错误检测. 增强的算法在具有挑战性的环境条件下实现了更好的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 野生动物监测 野生动物监测
背景情况:
- 夜间野生动物检测至关重要,但由于可见度差和恶劣天气,具有挑战性.
- 现有的算法在雨天条件下经常难以准确,导致错误检测.
研究的目的:
- 开发一个先进的野生动物目标检测算法,以提高在雨夜环境中的准确性.
- 解决当前在恶劣天气条件下检测系统的局限性.
主要方法:
- 一个改进的YOLOX网络,包括MobileViT-Pooling,动态头和焦点IoU模块.
- 移动ViT-Pooling增强了功能提取和全球信息传感.
- 动态头提高了检测头的表示,焦点IOU优化了损失函数.
主要成果:
- 实现了87.8%的mAP@0.5 (7.9%的改善) 和62.0%的mAP@0.5:0.95 (5.3%的改善).
- 在雨天条件下,在夜间野生动物检测准确度显著增加.
- 在具有挑战性的环境中成功降低了错误检测率.
结论:
- 拟议的算法在恶劣的降雨条件下大幅改善了夜间野生动物检测.
- 专用模块的集成有效地提高了检测准确性和稳定性.
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