计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240200107-6.doi: 10.11896/jsjkx.240200107

• 图像处理&多媒体技术 • 上一篇    下一篇

一种EO-YOLOX输电线绝缘子检测方法

胡益民1,2, 曲光3, 王夏兵4, 张杰4, 李加东1,2   

  1. 1 中国科学院苏州纳米技术与纳米仿生研究所 江苏 苏州 215123
    2 中国科学院多功能材料与轻巧系统重点实验室 江苏 苏州 215123
    3 空军通信士官学校地空导航系 辽宁 大连 116600
    4 郑州轻工业大学电气信息工程学院 郑州 450002
  • 出版日期:2024-11-16 发布日期:2024-11-13
  • 通讯作者: 曲光(quhn1234@163.com)
  • 作者简介:(ymhu2015@sinano.ac.cn)
  • 基金资助:
    国家自然科学基金(62102373)

EO-YOLOX Model for Insulators Detection in Transmission Lines

HU Yimin1,2, Qu Guang 3, WANG Xiabing4, ZHANG Jie 4, LI Jiadong 1,2   

  1. 1 Suzhou Institute of Nano-tech and Nano-bionic,Chinese Academy of Sciences,Suzhou,Jiangsu 215123,China
    2 Key Laboratory of Multifunctional Nanomaterials and Smart Systems,Chinese Academy of Sciences,Suzhou,Jiangsu 215123,China
    3 Department of Ground-to-Air Navigation,Air Force Communication NCO Academy,Dalian,Liaoning 116600,China
    4 College of Electric and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:HU Yimin,born in 1987,master,engineer,is a member of CCF(No.T4466M).His main research interests include machine vision,bionic robots,and multi-sensor data fusion.
    QU Guang,born in 1984,master,lectu-rer.His main research interests include image processing and object detection.
  • Supported by:
    National Natural Science Foundation of China(62102373).

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摘要： 为了保证电力系统的安全运行,使用无人机巡检技术对高压绝缘子进行日常检查是必要的。然而,受到电力线磁场和飞行安全的影响,图像数据中绝缘子像素表征减少,进而导致绝缘子检测的准确性降低。针对上述问题,提出了一种有效优化YOLOX(Efficient Optimization YOLOX,EO-YOLOX)检测模型。该模型首先利用空洞卷积(Atrous Convolution)的思想,提出了空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块,消除图像中的无关信息,提高了网络识别感兴趣区域的能力。其次,在特征融合阶段加入了注意特征融合(Attentione Feature Fusion,AFF)模块,通过向融合特征图中补充深层语义和浅层细节信息,提高了检测绝缘子的准确性。最后,针对传统损失函数不能准确反映两个边界框之间距离的问题,提出了一种优化损失函数,以更准确地评估边界框的质量。将该算法在绝缘子数据集上进行了实验和测试,结果表明,与传统的 YOLOX 方法相比,该算法在识别绝缘子方面表现优异,mAP 值提高了约 2.59%。该模型的实时处理效率高达41.21帧每秒,有效解决了绝缘子检测难题。

关键词: 绝缘子检测, 空洞卷积, EO-YOLOX, 绝缘子数据集

Abstract: To ensure the safe operation of the power system,daily inspection of high voltage insulators using UAV inspection techniques is necessary.However,the influence of power line magnetic field and flight safety leads to a reduction of insulator pixel representation in the image data,which in turn reduces the accuracy of insulator detection.To address these issues,this paper proposes an efficient optimization YOLOX(EO-YOLOX) detection model.Firstly,the model makes use of the idea of atrous convolution and proposes the atrous spatial pyramid pooling(ASPP) module,which eliminates the irrelevant information in the image and improves the ability of the network to identify the region of interest.Secondly,the attentione feature fusion(AFF) module is ad-ded to the feature fusion stage,which improves the accuracy of detecting insulators by supplementing deep semantic and shallow detail information into the fused feature map.Finally,for the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes,this paper proposes an optimised loss function to more accurately assess the quality of the bounding boxes.Experiments and tests are carried out on the insulator dataset,and the experiment results show that the proposed algorithm performs excellently in identifying insulators,with an improvement of about 2.59% in mAP value,compared with the traditional YOLOX method.The real-time processing efficiency of the model is as high as 41.21 frames per second,which effectively solves the insulator detection problem.

Key words: Insulator detection, Atrous convolution, EO-YOLOX, Insulator dataset

中图分类号: 

  • TP391.41
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