ESC-YOLOv8:一个增强的深度学习框架,用于对单线图形象的语义理解
Hina Bhanbhro1, Yew Kwang Hooi1, Worapan Kusakunniran2
1Faculty of Science, Management & Computing, Universiti Teknologi Petronas Seri Iskandar, Seri Iskandar, Perak Darul Ridzuan, Malaysia.
PloS one
|March 11, 2026
概括
本研究介绍了一种混合深度学习模型,用于在电气单线图 (SLD) 中对符号进行分类. 新方法提高了分析复杂电气系统图的准确性和效率.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 单线图 (SLD) 对于电气系统分析至关重要,但手动解释是低效的,容易出错.
- 现有的深度学习模型在SLD中的细粒度符号细节中扎,导致错误分类.
研究的目的:
- 开发一种优化的混合深度学习方法,用于在SLD中准确的符号分类.
- 为了增强细粒度符号细节的识别,并在混乱的图形区域中提高性能.
主要方法:
- 一种新的混合深度学习方法,集成混合残留注意力模块 (HRAM) 和近距离感知损失函数.
- 在新创建的SLD数据集上对最先进的深度学习模型进行基准测试.
主要成果:
- 达到93.5%的平均精度 (mAP),比最佳基线提高3.8%.
- 模型参数减少了19.6%,提高了计算效率.
- 在分类符号方面表现出卓越的性能,特别是在复杂和混乱的SLD区域.
结论:
- 拟议的混合深度学习方法在SLD的语义处理方面取得了重大进展.
- 这种方法可以更有效,更准确地分析电气系统图,提高运行安全和效率的洞察力.
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