摘要
【目的】随着电网规模的不断扩大和运行环境的日益复杂,输电设备表面腐蚀问题已成为威胁电网安全运行的重要因素。传统人工巡检方式不仅效率低,而且难以准确识别设备表面的细微腐蚀特征,特别是在复杂自然环境下腐蚀区域边界往往模糊不清,给精确识别带来巨大挑战。为此,提出了一种基于图像语义分割的输电设备表面腐蚀区域细粒度识别方法,旨在通过深度学习技术实现对腐蚀区域的精准检测和识别。【方法】上述识别方法的核心为构建了一个融合注意力机制的语义分割网络,该网络通过引入通道注意力和空间注意力机制,能够有效捕捉腐蚀区域的细微特征和精确边界。具体而言,通道注意力机制通过分析特征图各通道间的关系,增强对腐蚀特征显著通道的响应;空间注意力机制则通过关注特征图的空间位置信息,强化腐蚀区域的空间特征表达。完成初步分割后,采用K-means++聚类算法对分割后图像的像素RGB值进行聚类分析,该算法通过优化初始聚类中心的选择,有效避免了传统K-means算法可能陷入局部最优的问题,从而能够更准确地划分腐蚀与未腐蚀区域。为进一步提高识别精度,引入了结构相似性指标对各聚类区域进行评估,通过计算区域间的结构相似度,在像素级别上实现了腐蚀区域的细粒度识别。【结果】识别方法在复杂自然环境下的输电设备图像数据集上取得了显著效果,腐蚀区域识别准确率大幅度提高,边界定位精度较传统方法明显提升。【结论】该识别方法通过融合注意力机制的语义分割网络,并结合K-means++聚类算法与结构相似性指标评估,为输电设备表面腐蚀区域的细粒度识别开创了一种高效且精确的新途径。该识别方法通过引入注意力机制,有效应对了腐蚀区域特征复杂、边界模糊的挑战,显著提升了识别精度。同时结合聚类算法与结构相似性评估,实现了像素级别的细致区分,进一步增强了识别的精细度和实用性,为电网的安全监测与维护提供了坚实的技术保障,不仅保障了电网的安全稳定运行,还为图像识别和分割技术在其他领域的应用提供了宝贵的思路和启示。
[Objective]With the continuous expansion of power grid scale and the increasing complexity of the operating environment,surface corrosion of transmission equipment has become a critical factor threatening the safe operation of power grids.Traditional manual inspection methods are not only inefficient but also struggle to accurately identify subtle corrosion features on equipment surfaces,especially in complex natural environments where the boundaries of corrosion areas are often blurred,posing significant challenges for precise recognition.To address this,a fine-grained recognition method for corrosion areas on the surface of transmission equipment based on image semantic segmentation was proposed,aiming to achieve precise detection and recognition of corrosion areas through deep learning technology.[Methods]The core of this method was the construction of a semantic segmentation network integrated with an attention mechanism.This network,by introducing both channel attention and spatial attention mechanisms,could effectively capture the subtle features and precise boundaries of corrosion areas.Specifically,the channel attention mechanism enhanced the response to channels with prominent corrosion features by analyzing the relationships among various channels in the feature map.Meanwhile,the spatial attention mechanism strengthened the spatial feature representation of corrosion areas by focusing on the spatial location information in the feature map.After the initial segmentation,the K-means++clustering algorithm was employed to perform clustering analysis on the RGB values of the pixels in the segmented images.By optimizing the selection of initial clustering centers,this algorithm effectively avoided the issue of local optimum that could arise with the traditional K-means algorithm,thereby more accurately dividing corroded and uncorroded areas.To further improve recognition accuracy,the structural similarity index was introduced to evaluate each clustered area,and fine-grained recognition of corrosion areas was achieved at the pixel level by calculating the structural similarity between areas.[Results]Experimental results demonstrate that the proposed method exhibits remarkable performance on a dataset of transmission equipment images in complex natural environments,achieving a significantly improved corrosion area recognition accuracy and an obvious improvement in boundary localization accuracy compared to traditional methods.[Conclusion]In summary,the semantic segmentation network integrated with an attention mechanism,combined with the K-means++clustering algorithm and SSIM evaluation,pioneers an efficient and precise new approach for fine-grained recognition of corrosion areas on the surface of transmission equipment.By incorporating the attention mechanism,the proposed method effectively addresses the challenges posed by complex corrosion features and blurred boundaries,significantly enhancing recognition accuracy.Meanwhile,the combination of the clustering algorithm and SSIM evaluation enables pixel-level detailed differentiation,further improving the fineness and practicality of recognition and providing solid technical support for the safe monitoring and maintenance of power grids.Not only does the proposed method ensure the safe and stable operation of power grids,but it also offers valuable insights and inspiration for the application of image recognition and segmentation technologies in other fields.
作者
陈伯建
吴文斌
林承华
梁曼舒
吴晓杰
CHEN Bojian;WU Wenbin;LIN Chenghua;LIANG Manshu;WU Xiaojie(College of Control Science and Engineering,Zhejiang University,Hangzhou 310058,Zhejiang,China;Electric Power Research Institute,State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350007,Fujian,China)
出处
《沈阳工业大学学报》
北大核心
2025年第3期339-347,共9页
Journal of Shenyang University of Technology
基金
福建省工业引导性重点项目(2024H0037)
国家电网有限公司科技项目(521304230011)。
关键词
语义分割
输电设备
表面腐蚀区域
细粒度识别
注意力机制
图像识别
K-means++聚类算法
结构相似性
semantic segmentation
transmission equipment
surface corrosion area
fine-grained recognition
attention mechanism
image recognition
K-means++clustering algorithm
structural similarity
