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一个基于改进的YOLOv5s的高效检测模型,用于检测鱼类异常表面特征
Zheng Zhang1, Xiang Lu1, Shouqi Cao1
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究引入了一种改进的YOLOv5s模型,用于实时检测异常鱼的表面特征. 改进后的模型实现了高准确性和速度,克服了水生动物健康监测现有方法的局限性.
科学领域:
- 水产养殖是水产养殖的一种方式.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确检测异常鱼的表面特征对于识别水生健康问题至关重要.
- 目前的方法存在主观性,准确性低,实时性能差等问题.
研究的目的:
- 开发一个实时,准确的鱼表面异常检测模型.
- 为了解决现有的鱼类健康监测技术的局限性.
主要方法:
- 一个改进的YOLOv5s模型,结合了优化的完整交叉点 (CIoU) 和非最大抑制 (NMS),使用正常化的高斯瓦瑟斯坦距离来检测微小的目标.
- 整合了DenseOne模块以实现功能重复使用,以及MobileViTv2以提高功能提取网络中的检测速度.
- 基于ACmix原理的全维动态卷积和卷积块注意模块的融合,用于复杂背景中的深度特征提取.
主要成果:
- 该模型在160个验证组中实现了99.5%的精度,99.1%的回忆,99.1%的mAP50,73.9%的mAP50:95和88 FPS.
- 与基线相比,性能改善包括+1.4%的精度,+1.2%的回忆,+3.2%的mAP50,+8.2%的mAP50:95和+1的FPS.
- 与其他最先进的模型相比,增强型号表现出优越的性能.
结论:
- 建议改进的YOLOv5s模型在实时和准确检测异常鱼类表面特征方面取得了重大进展.
- 该模型的改进有效地解决了与水生动物健康评估的主观性,准确性和速度相关的挑战.
- 这种方法为改善鱼类健康监测和水产养殖管理提供了一个强大的工具.
关键词:
在ACmix中使用ACmix.在Densone模块中使用Densone模块.移动ViTv2模块是一个模块.在ODC-CBAM中使用ODC-CBAM.这是YOLOv5s.鱼的异常表面特征 鱼的异常表面特征规范化的高斯瓦瑟斯坦距离度量.更多相关视频
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