基于改进YOLOv7的电力调度控制系统图元识别方法研究

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摘要针对电力调度控制系统图中图元分布密集、图元相似和小尺寸图元较多导致识别效果差的问题,本文提出一种基于改进YOLOv7的电力调度控制系统图元识别方法。首先,使用轻量化的DRBNCSPELAN模块替换主干网络中的ELAN模块,以同时捕获不同尺度的空间模式;其次,提出一种信息引导融合模块,替代颈部网络中的Concat,并融合SE注意力机制,以增强信息全局交互能力;接着,引入MPDIoU损失函数,以改善图元边界框的识别效果;最后,利用电力调度控制系统图数据集进行验证。结果表明,与基准模型相比,所提方法的精确率、召回率和平均精确率均值分别提高了5.1个百分点、3.1个百分点和3.5个百分点,有助于实现对电力调度控制系统图元的精准识别。

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	张文广
	曾祥玖
	刘重阳

关键词 ：深度学习, 电力调度控制系统图, 图元识别, YOLOv7, SE注意力机制

Abstract：Aiming at the problems of dense distribution of elements, similar elements and large number of small-sized elements in power dispatching control system diagrams, which lead to poor recognition effect, an improved you only look once v7 (YOLOv7) element recognition method for the power dispatching control system diagrams is proposed. Firstly, the lightweight dilated reparam block net with cross stage partial and efficient layer aggregation network (DRBNCSPELAN) module is used to replace the efficient layer aggregation network (ELAN) module in the backbone network to capture spatial patterns of different scales simultaneously. Secondly, an information-guided fusion module is proposed to replace the Concat in the neck network, and the sequeeze-and-excitation (SE) attention mechanism is integrated to enhance the global information interaction ability. Then, the minimum point distance intersection over union (MPDIoU) loss function is introduced to improve the recognition effect of the element bounding box. Finally, experimental validation is performed via the power dispatch control system diagram dataset. The results show that compared with the baseline model, the precision, recall and mean average precision of the proposed method are improved by 5.1 percentage points, 3.1 per-centage points and 3.5 percentage points respectively, which is helpful to achieve accurate recognition of elements in the power dispatching control system diagrams.

Key words： deep learning power dispatching control system diagrams element recognition you only look once v7 (YOLOv7) sequeeze-and-excitation (SE) attention mechanism

收稿日期: 2024-12-27

作者简介: 张文广（1975—）,男,山东省烟台市人,教授,博士,研究方向为人工智能技术在电力系统的应用。

引用本文:

张文广, 曾祥玖, 刘重阳. 基于改进YOLOv7的电力调度控制系统图元识别方法研究[J]. 电气技术, 2025, 26(5): 1-9. ZHANG Wenguang, ZENG Xiangjiu, LIU Chongyang. Research on element recognition method of power dispatching control system based on improved YOLOv7. Electrical Engineering, 2025, 26(5): 1-9.

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https://dqjs.cesmedia.cn/CN/Y2025/V26/I5/1

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