研究基于Yolov8s改进的团伙目标检测算法
Zhibo Fu1, Xinpeng Yuan1, Zhengkun Xie1
1School of Coal Engineering, Shanxi Datong University, Datong, China.
PloS one
|July 9, 2024
概括
这项研究引入了一种增强的深度学习算法来检测煤炭,显著提高速度和准确性,同时降低模型大小和计算成本,以实现高效的分类.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对于煤团目标检测的深度学习模型面临的挑战是速度缓慢,参数数量高和显著的计算成本.
- 优化这些模型对于开发高效和实时的帮派分类系统至关重要.
研究的目的:
- 开发基于Yolov8s的改进深度学习算法,用于煤目标检测.
- 为了提高检测速度,减少模型复杂性,并提高与现有方法相比的准确性.
主要方法:
- 使用Fasternet作为轻量级的骨干来增加对象检测速度并降低模型复杂性.
- 在HEAD模块中用C2F取代Slimneck,并在Detect层使用Detect-DyHead来提高准确性.
- 集成的DIoU损失功能和BAM阻断注意力机制,以增强功能焦点和检测性能.
主要成果:
- 在模型存储尺寸方面实现了28%的减少.
- 将参数数量减少了28.8%,计算量减少了34.8%.
- 与原来的Yolov8s模型相比,检测准确度提高了2.5%.
结论:
- Yolov8s变换模型提供了一个快速,实时和高效的解决方案来检测和分类煤炭.
- 这一进步为智能排序煤炭提供了强有力的支持,解决了以前深度学习方法的关键局限性.
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