YOLOv8-POS:一种用于煤岩图像识别的轻量级模型
1School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
PeerJ. Computer science
|June 26, 2025
概括
一个新的煤石图像识别模型,YOLOv8-POS,可以减少错误检测和复杂性. 它以更少的参数和计算实现了高精度,改善了实际应用.
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:
- 煤岩图像识别面临诸如由于图像质量和遮蔽导致的错误检测等挑战.
- 现有的模型往往具有很高的复杂性,限制了在工业环境中的实际部署.
研究的目的:
- 推出YOLOv8-POS,一种用于准确和高效的煤岩图像识别的新方法.
- 为了减少模型复杂性和计算需求,同时提高检测性能.
主要方法:
- 开发了一个C2f-PConv模块,将C2f和部分卷积 (PConv) 结合起来,用于选择性通道处理.
- 整合了一个重叠的空间减少注意力模块,以优化空间特征融合.
- 采用了轻量级模块的薄项设计,以减少计算和存储需求.
主要成果:
- 在煤岩数据集上,YOLOv8-POS实现了77.1%的AP50和63.6%的AP50:95.
- 显著降低模型参数至2.60M,并将FLOPS降低至6.4G.
- 在比较评估中,与其他知名算法相比,表现出优越的性能.
结论:
- YOLOv8-POS为煤岩图像识别提供了一个高度准确和高效的解决方案.
- 该模型的复杂性降低提高了其在现实世界的场景中的实际应用性.
- 这种精细的方法为工业部署带来了重大优势.
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