基于深度学习和灰度纹理特征的铁路接触网绝缘子状态检测

铁路接触网绝缘子状态检测对铁路行车安全有着 重大的意义,为解决目前人工对绝缘 子图像检测结果的不确定性,提出一种深度学习结合灰度纹理特征的检测方法。首先使用 Faster R-CNN (faster region-based convolutional neural network)目标检测算法对图像中绝缘子精确识别,再通过灰度共生矩阵对绝缘子纹理 特征进行分析提取,之后结合支持向量机将绝缘子分为正常绝缘子和异常绝缘子,实验数 据结果证明使用能量、熵、相关度3种纹理特征进行绝缘子状态分类时对实验数据中的正 常状态绝缘子的分类精度可达100%,异常状态绝缘子的分类精度达97.5%,最后依据绝缘 子图像灰度分布的周期性特点,利用灰度积分投影将异常绝缘子分为破损绝缘子和夹杂异 物绝缘子。实验结果表明所提方法可以有效对绝缘子状态进行检测分类。

State detection of railway catenary insulators based on deep learning and gray- scale texture features

The state detection of railway catenary insulators is of great signifi cance to the safety of railway traffic.To solve the uncertainty of manual inspection on insulator i nspection results,a detection method combining deep learning and gray texture features are proposed. First,the Faster R-CNN (faster region-based convolutional neural network) algorithm is used to accurately identify the insulators in the image,and then the texture features of the insulators are analyzed and extracted through the gray-level co -occurrence matrix. Then,the support vector machine is used to divide the insulators into normal in sulators and abnormal insulators.The result of the experimental data proves that the classif ication accuracy of the normal insulators in the experimental data can reach 100%,and the classific ation accuracy of the abnormal insulators can reach 97.5% when the three texture features of energ y,entropy and correlation are used to classify the insulator state.Finally,according to the periodic characteristics of the gray distribution of the insulator image,the abnormal insulators are div ided into damaged insulators and foreign matter insulators by gray-level integration projection. Experimental results have showed that the proposed method can effectively detect and classify the state of insulators.