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Insulator Fault Detection in Aerial Images based on the Mixed-grouped Fire Single-shot Multibox Detector

机译：基于混合分组的火射击多杆多焦点探测器的空中图像中的绝缘体故障检测

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摘要

In a complex background, insulator fault is the main factor behind transmission accidents. With the wide application of unmanned aerial vehicle (UAV) photography, digital image recognition technology has been further developed to detect the position and fault of insulators. There are two mainstream methods based on deep learning: the first is the "two-stage" example for a region convolutional neural network and the second is the "one-stage" example such as a single-shot multibox detector (SSD), both of which pose many difficulties and challenges. However, due to the complex background and various types of insulators, few researchers apply the "two-stage" method for the detection of insulator faults in aerial images. Moreover, the detection performance of "one-stage" methods is poor for small targets because of the smaller scope of vision and lower accuracy in target detection. In this article, the authors propose an accurate and real-time method for small object detection, an example for insulator location, and its fault inspection based on a mixed-grouped fire single-shot multibox detector (MGFSSD). Based on SSD and deconvolutional single-shot detector (DSSD) networks, the MGFSSD algorithm solves the problems of inaccurate recognition in small objects of the SSD and complex structure and long running time of the DSSD. To resolve the problems of some target repeated detection and small-target missing detection of the original SSD, the authors describe how to design an effective and lightweight feature fusion module to improve the performance of traditional SSDs so that the classifier network can take full advantage of the relationship between the pyramid layer features without changing the base network closest to the input data. The data processing results show that the method can effectively detect insulator faults. The average detection accuracy of insulator faults is 92.4% and the average recall rate is 91.2%. (C) 2021 Society for Imaging Science and Technology.
机译：在一个复杂的背景中，绝缘体故障是传输事故背后的主要因素。随着无人驾驶飞行器（UAV）摄影的广泛应用，进一步开发了数字图像识别技术来检测绝缘体的位置和故障。基于深度学习存在两个主流方法：首先是区域卷积神经网络的“两阶段”示例，第二个是“单级”示例，例如单次Multibox检测器（SSD），两者其中构成了许多困难和挑战。然而，由于复杂的背景和各种类型的绝缘体，很少有研究人员应用用于检测空中图像中的绝缘子故障的“两级”方法。此外，由于较小的视觉范围和目标检测的准确度较小，“单级”方法的检测性能对于小目标差。在本文中，作者提出了一种准确和实时方法，用于小对象检测，绝缘体位置的示例，以及基于混合分组的火单射多射门探测器（MGFSSD）的故障检查。基于SSD和DECONVOOLLOOLLE-SHOT检测器（DSSD）网络，MGFSSD算法解决了SSD的小对象中识别不准确的问题，以及DSSD的长期运行时间。要解决某些目标的问题重复检测和小目标缺失检测原始SSD，作者介绍了如何设计有效和轻质的特征融合模块，以提高传统SSD的性能，以便分类器网络可以充分利用金字塔层特征之间的关系而不改变最接近输入数据的基础网络。数据处理结果表明，该方法可以有效地检测绝缘体故障。绝缘体故障的平均检测精度为92.4％，平均召回率为91.2％。 （c）2021年成像科技协会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2021年第3期|030402.1-030402.10|共10页
  • 作者

    Chen Songbo; Su Chao; Kuang Zhenxing; Ouyang Ye; Gong Xiang;

  • 作者单位

    Guangdong Power Grid Corp Qingyuan Guangdong Peoples R China|Zhejiang Univ Hangzhou Peoples R China;

    Guangdong Power Grid Corp Qingyuan Guangdong Peoples R China|Zhejiang Univ Hangzhou Peoples R China;

    Guangdong Power Grid Corp Qingyuan Guangdong Peoples R China;

    Guangdong Power Grid Corp Qingyuan Guangdong Peoples R China;

    Guangdong Power Grid Corp Qingyuan Guangdong Peoples R China;

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