The state detection of railway catenary insulators is of great significance to the safety of railway traffic.To solve the uncertainty of manual inspection on insulator inspection 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 insulators and abnormal insulators.The result of the experimental data proves that the classification accuracy of the normal insulators in the experimental data can reach 100%,and the classification accuracy of the abnormal insulators can reach 97.5% when the three texture features of energy,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 divided 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.