Aiming at the problem that the proportion of insulators in power equipment images is small and they are easy to miss detection

this paper proposes an insulator detection method based on cross-connected convolutional neural network for the power equipment image. Firstly

the convolutional layers of the last three layers of the network are connected with the fully-connected layer in the regional proposal network(RPN)

and the three-layer convolution features are simultaneously sent to the classification layer and the regression layer to obtain a series of high quality insulator candidate regions. Secondly

the region proposals are input into the insulator detection sub-network

and the region of interest(ROI)features of candidate regions are sent to the cascaded Adaboost classifier to detect insulators. Evaluations are performed and comparative experiments are conducted based on the candidate region generation methods. The results show that the candidate regions obtained by the proposed method have high recall rates and they focus more on the positions of the insulators

and the accuracy of the insulator detection is 10% higher than the conventional methods. The proposed method can effectively recognize and locate insulators of different sizes with complex background.