In order to solve the problem of underreporting in the inspection of defects by transmission line machine

we proposed a defect recognition method of power transmission with a low underreporting rate based on combined deep target detection framework. In this method

first

two target detection algorithms were used to train on the transmission line inspection image data set to obtain the feature extraction network for transmission line equipment defects. Then

the location random distribution function was introduced to improve the method of target prediction

and the adaptive non-maximum suppression discriminator was used to adaptively fuse the feature extraction results of the two networks to finally obtain the type and location of the defects in the inspection picture. The test results show that this method can be adopted to effectively reduce the underreporting rate of inspection image defect recognition. The average underreporting rate of major defects is far lower than that obtained by other deep learning models; meanwhile

the detection of many kinds of defects can be simultaneously realized

which can effectively promote the application and promotion of automatic defect identification in the routine inspection of transmission lines.