To solve the problems of poor positioning accuracy and stability of existing insulators identified by UAV aerial photography transmission lines

a Libra-RCNN insulator detection model based on ASFF pyramid network is proposed. Firstly

the FRN normalized layer is used to replace the original BN layer to eliminate the dependence of the normalized layer on the size of training batch and increase the learning efficiency of the model.Then

ASFF network structure is introduced into Libra-RCNN algorithm pyramid to effectively solve the problem of inconsistency inside the feature pyramid.Finally

GIoU crossover ratio is used to replace the original IoU crossover ratio to better accurate insulator position.In Insulators＿Datasets data set

the average accuracy of improved Libra-RCNN model is 94.10%

and the recall rate is 97.51%.Compared with the original Libra-RCNN model

the improvement rate is 2.23% and 2.61% respectively

which indicates that the proposed algorithm can identify insulators stably and effectively.