An InceptionNet-based object detection algorithm for wind turbine inspection robot

Wind power has developed rapidly in recent years, and regular inspection of wind turbines can guarantee their normal operation. The use of inspection robots can improve inspection efficiency and reduce risks. The traditional way mainly relies on the manual operation of the UAV to search the wind turbines, which still has the problem of low efficiency. For the object search problem in aerial images, this paper proposes a wind turbine object detection algorithm based on the Inception Network, which uses artificial intelligence to extract wind turbine objects in the images. For the problem of excessive parameters in the baseline algorithm, this paper replaces the original backbone network to reduce the number of parameters. For the problem of overfitting, the paper adopts the strategy of Batch Normalization to improve the generalization ability. After the test using the NAIP dataset, the algorithm achieves the accuracy of 93.10% on the wind turbine object detection task. The results show that the object detection algorithm based on Inception Network can achieve better results and has certain robustness and generalization ability for objects in different environments.