The military optical cable network is an important communication infrastructure for army.The traditional artificial walking patrol and inspection is the main method to find the hidden dangers of the optical cable lines.However

it takes a long time

and consumes a lot of manpower and material resources.It is also vulnerable to the changes in laying methods and terrain environment.However

the use of unmanned aerial vehicle for the patrol and inspection of optical cable lines

is the main development direction in the future due to the characteristics of fast

safe and economical.Because the factor of excavation in construction is the most important reason for the obstacle of the optical cable lines

it is proposed to apply the deep learning Faster Region-based Convolutional Neural Network（Faster R-CNN）target detection method to detect the image of engineering vehicle obtained by unmanned aerial vehicle.Based on the Vehicle Detection in Aerial Imagery（VEDAI）public data set

the engineering vehicle data set is made.Through simulation

the target detection of engineering vehicles such as excavators and bulldozers in aerial photography is achieved.The Average Precision（AP）value can reach 0.659.The detection results are better than traditional Deformable Part Model（DPM）and Histogram of Oriented Gradient+ Local Binary Pattern+ Support Vector Machine（HOG+LBP+SVM）machine learning detection methods.The study can provide a certain reference for the application of patrol and inspection for military optical cable lines by unmanned aerial vehicle.