JUHCCR-v1:用于手绘电路和电子电路组件识别的数据库
Ayush Roy1, Saptarshi Pani1, Samir Malakar2
1Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India.
Scientific reports
|November 4, 2025
概括
研究人员开发了一套新的数据集,用于识别手绘的电气元件. 一个深度学习模型实现了91.15%的准确性,推进了自动电路分析.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 从手绘电路中识别电气和电子元件是一个重大挑战.
- 缺乏公开可用的数据集阻碍了这一研究领域的进展.
研究的目的:
- 为了解决手绘电路组件识别数据集的缺乏.
- 开发和发布一个全面的数据集,用于训练深度学习模型.
- 建立用于电路组件识别的基线性能.
主要方法:
- 创建了JUHCCR-v1,一个由20个常见的手绘电路组件组成的数据集.
- 创建了一个合成数据集,其中包含组件的变化 (定向,扭曲).
- 开发了一种使用DenseNet-121架构与卷积块注意模块的加权合奏方法.
主要成果:
- 开发的数据集 (JUHCCR-v1) 和合成变异是公开的.
- 该比较方法在测试组图像上获得了91.15%的准确性.
- 公开可用的数据集和代码旨在促进进一步的研究.
结论:
- 创建JUHCCR-v1数据集是迈向自动化电路分析的关键一步.
- 拟议的深度学习方法为组件识别提供了强有力的基准.
- 公开发布数据集和代码将促进社区推动该领域的进步.
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