YOLOv8-Coal:基于改进的YOLOv8的煤岩图像识别方法
Wenyu Wang1, Yanqin Zhao1, Zhi Xue2
1School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
PeerJ. Computer science
|September 24, 2024
概括
一种新的YOLOv8-Coal方法通过解决低光和遮蔽问题,提高了煤岩图像识别的准确性和速度. 这种增强的物体检测模型为工业应用提供了更好的性能和效率.
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:
- 煤岩图像识别面临的挑战包括由于照明不良,失焦和遮蔽导致的错误检测和遗漏.
- 现有的方法在复杂的工业环境中难以达到高精度和加工速度.
研究的目的:
- 推出YOLOv8-Coal,一种增强的物体检测模型,用于改进煤石图像识别.
- 通过增强特征提取,特征融合和模型效率来解决现有方法的局限性.
主要方法:
- 使用YOLOv8作为基础架构.
- 集成的可变形卷积网络v3用于自适应性特征提取.
- 实现了一个极化自我注意模块,用于精细的功能融合.
- 引入了一个C2fGhost模块,以减少模型复杂性和计算负载.
主要成果:
- 在煤岩图像数据集上,YOLOv8-Coal在AP50 (77.7%),AP50:95 (62.8%) 和AR50:95 (75.0%) 中取得了显著的改进.
- 将模型参数减少到2.59M,FLOP减少到6.9G,模型重量文件大小为5.2 MB.
- 与其他常用的物体检测算法相比,表现出卓越的性能.
结论:
- YOLOv8-Coal有效地提高了煤岩图像的识别精度和处理速度.
- 模块的新组合导致了更高效,更准确的物体检测系统.
- 这种方法为需要强大的图像识别的工业应用提供了有前途的解决方案.
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