研究基于改进的Yolov5的轻量级算法来检测带
Xinpeng Yuan1, Zhibo Fu2, Bowen Zhang3
1School of Coal Engineering, Shanxi Datong University, Datong, 037000, China. XpnYuan@163.com.
Scientific reports
|March 21, 2024
概括
这项研究引入了一种改进的深度学习算法来检测煤炭,显著提高速度和减少模型复杂性. 优化方法为智能煤炭路分类系统提供了实用参考.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对于帮派检测的深度学习方法面临的挑战是速度慢,参数众多,计算要求高.
- 现有的模型需要优化,以便在智能煤炭加工中实现高效的实时应用.
研究的目的:
- 开发一个优化的深度学习算法,以实现更快,更有效的团伙目标检测.
- 为了减少模型复杂性,参数数量和计算成本,同时保持或提高检测性能.
主要方法:
- 拟议的算法使用Yolov5s作为基础,将EfficientViT轻量级网络作为骨干.
- 修改包括将C3模块替换为C3_Faster,删除20x20特征地图分支,并实现MpIoU损失函数.
- 引入了一种SE关注机制,以加强对关键特征的关注.
主要成果:
- 改进后的模型实现了77.8%的尺寸和计算成本降低.
- 参数数量减少了78.3%,检测速度增加了30.6% (每秒).
- MpIoU损失函数和SE注意力机制有助于提高检测准确度.
结论:
- 增强的Yolov5s算法为煤炭检测的效率提供了显著的改进.
- 优化模型为智能煤炭路分类提供了可行的解决方案,解决了以前的局限性.
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