Research on Target Detection of Excavator in Aerial Photography Environment based on YOLOv4

In order to test the deep-learning YOLOv4 target detection algorithm's effect on excavator target detection in aerial photography environment, a fast target detection method for excavators in aerial photography environment was simulated. Based on the Pytorch deep learning framework and the deep learning YOLOv4 (You Only Look Once version 4) target detection algorithm, through the production of excavator aerial photography data sets, the simulation realized the rapid target detection of excavators in the aerial photography environment, and its target recognition AP value reached 0.72. Based on the same data set, its detection accuracy and speed exceeded Traditional algorithms such as Faster RCNN are used. The simulation results can provide a certain reference for the research of aerial inspection and maintenance of underground pipeline facilities.