In the patrol of transmission lines

key power components often have such defects as corrosion and shedding

resulting in false and missed detection as well as great increase of labor costs. In view of such problem

a kind of improved algorithm based on YOLOv6 is proposed in this paper. The RepFPN structure is mainly added in the neck part

and a bottom-up path aggregation network is added to enhance the expression ability of the output features and improve the performance of the model. However

it increases the computational cost. In order to reduce the amount of calculation

the RepVGG structure with a similar residual structure and rich gradient information is added. The experimental data show that when three RepVGG structures are used in replace of C3 layer

the accuracy of 1-2 points will be improved. At the same time

after the fusion of operator of ConvBNAct

only one Conv + inplace activation is needed to complete the original three times of Op calculations. In most cases

the identical numerical results can be obtained

which alleviates the problem of increasing the amount of calculation in the above improvements. The experiment shows that the improved model

mAP

_0.5: 0.95 is increased by 12%

the accuracy is increased by 3.9%

and each

loss

value is significantly reduced and

at the same time

various defects of power components and foreign objects on transmission lines are well detected.