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Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

机译：通过霍夫变换提高红外图像中电力套管的神经网络检测精度

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摘要

To improve the neural network detection accuracy of the electric power bushings in infrared images, a modified algorithm based on the You Only Look Once version 2 (YOLOv2) network is proposed to achieve better recognition results. Specifically, YOLOv2 corresponds to a convolutional neural network (CNN), although its rotation invariance is poor, and some bounding boxes (BBs) exhibit certain deviations. To solve this problem, the standard Hough transform and image rotation are utilized to determine the optimal recognition angle for target detection, such that an optimal recognition effect of YOLOv2 on inclined objects (for example, bushing) is achieved. With respect to the problem that the BB is biased, the shape feature of the bushing is extracted by the Gap statistic algorithm, based on K-means clustering; thereafter, the sliding window (SW) is utilized to determine the optimal recognition area. Experimental verification indicates that the proposed rotating image method can improve the recognition effect, and the SW can further modify the BB. The accuracy of target detection increases to 97.33%, and the recall increases to 95%.
机译：为了提高电力套管在红外图像中的神经网络检测精度，提出了一种基于“一次只看一次”（YOLOv2）网络​​的改进算法，以达到更好的识别效果。具体地说，YOLOv2对应于卷积神经网络（CNN），尽管其旋转不变性很差，并且某些边界框（BB）表现出一定的偏差。为了解决该问题，利用标准的霍夫变换和图像旋转来确定用于目标检测的最佳识别角度，从而获得YOLOv2对倾斜物体（例如衬套）的最佳识别效果。针对BB偏斜的问题，基于K-均值聚类，通过Gap统计算法提取衬套的形状特征。之后，利用滑动窗口（SW）确定最佳识别区域。实验验证表明，所提出的旋转图像方法可以提高识别效果，而SW可以进一步修改BB。目标检测的准确性提高到97.33％，召回率提高到95％。

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