基于改进的YOLOv8的煤炭和河检测
Qingliang Zeng1, Guangyu Zhou1, Lirong Wan1
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
一种新的智能方法Our-v8增强了煤炭和道检测,用于实时分类. 它达到99.5%的准确性,同时保持轻量化和高效.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 煤炭分类面临着轻量化和实时检测要求的挑战.
- 现有的方法在不同的照明条件下可能缺乏准确性或效率.
研究的目的:
- 开发一个智能检测方法,用于煤炭和.
- 为了解决轻量化设计和实时处理煤炭分类的局限性.
主要方法:
- 提出了Our-v8,一个改进的YOLOv8模型用于煤炭和道检测.
- 使用拉普拉斯图像增强,CBAM注意力机制和EIOU损失功能.
- 在不同的照明条件下收集多样化的图像数据.
主要成果:
- 我们的v8在素灯环境中实现了99.5%的平均平均精度 (mAP).
- 该型号的轻量级为29.7 FLOPs,12.8 Param,尺寸为22.1 MB.
- 证明了准确的位置信息,用于煤炭和道检测.
结论:
- 我们的v8为实时煤炭分类提供了出色的性能和效率.
- 该方法适用于需要准确和快速检测的工业应用.
- 智能检测系统解决了煤炭加工中的关键挑战.
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