Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

Abstract

How to detect the data in the video image sequence and analyze it efficiently by using the artificial intelligence method is a frontline topic in the field of computer vision. Global wind energy resources are abundant, widely distributed, clean, and pollution-free and meet the requirements of sustainable economic and social development, so relying on the large-scale development and utilization of wind energy has been the common choice of many countries. A major goal of artificial intelligence research is to enable machines to perform complex tasks that normally require human intelligence to perform. The purpose of this study is to detect the video image sequence of wind power equipment based on artificial intelligence and analyze the effectiveness of the algorithm. In this study, augmented reality is used as an auxiliary means. According to the characteristics of the video image inside the fan, a moving target region detection algorithm based on the background difference method is proposed. The algorithm uses the difference between the current image and the background image, uses the first-order Kalman filter to update the dynamic background image, and then uses the adaptive threshold method to segment the moving region. After filtering, the moving target area can be obtained. The results show that the positive detection rate of 980 test samples is 99.6%, and the training time is only 3.785. It is concluded that the accuracy and the number of support vectors of this algorithm are better than other algorithms in the case of the same value of C. It shows that this study has a good detection effect. It provides an effective method for video image sequence detection of wind power equipment.

Retracted: Detection Algorithm of Wind Power Equipment Video Image Sequence Based on Artificial Intelligence

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”