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基于改进的YOLOv8的工业碳块实例细分算法
Runjie Shi1, Zhengbao Li2, Zewei Wu1
1College of Ocean Science and Engineering, Shandong University of Science and Technology, No 579, Qian Wan Gang Road, Qingdao, 266590, Qing Dao, China.
Scientific reports
|March 10, 2025
概括
本研究介绍了YOLOv8-HDSA,这是一个改进的实例细分算法,用于工业碳块识别. 它提高了识别碳块类型和细分边缘的准确性,这对于智能制造应用至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 机器视觉是工业智能应用的关键,特别是在自动化碳块清洁中.
- 精确的碳块类型识别和中心点定位对于现有的实例细分算法来说至关重要但具有挑战性.
研究的目的:
- 开发一个改进的实例细分算法 (YOLOv8-HDSA),用于精确的工业碳块识别和边缘细分.
- 增强特征表示和融合能力,以提高工业环境中的性能.
主要方法:
- 拟议的YOLOv8-HDSA算法采用采用哈达马德产物和扩展卷积的选择性强化特征融合模块 (SRFF).
- 整合了一个卷积式自我注意力机制,在头部有剩余结构,以改善特征提取.
- 引入了Focaler-IoU作为损失函数以优化回归性能.
主要成果:
- 在实际的工业数据集上,YOLOv8-HDSA显示了显著的改进.
- 在平均碳块识别准确度上实现了7.2%的提高.
- 碳块边缘细分精度提高了3.8%.
结论:
- 拟议的YOLOv8-HDSA算法有效地解决了工业碳块实例细分现有方法的局限性.
- 功能融合,注意力机制和损失功能的增强有助于卓越的识别和细分精度.
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