For object detection of the power tower base images taken by unmanned drones

a high-resolution image dataset of power tower bases was established

and an improved object detection algorithm based on Faster R-CNN（region-based convolutional neural networks） was proposed to improve the detection accuracy.Instead of VGG-16

a convolutional neural network architecture

this method uses ResNet-50 as the backbone network of Faster R-CNN and integrates multi-scale feature fusion techniques into the detection process.Finally

detection results of the power tower base detection algorithm based on this improved Faster R-CNN

YOLOv4

an object detection algorithm

and SSD（single shot multibox detector） were compared and analyzed. The results show that when the IoU（intersection over union） threshold is 0.75

the accuracy of the detection results of this algorithm averages a 2.9 percent increase

and when the IoU threshold is 0.5

the accuracy averages a 0.19 percent increase.