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The detection of foreign objects on transmission lines is an important research content of intelligent inspection in smart grid. The foreign objects on the transmission line tower will cause adverse effects on the transmission line and other equipment, and even endanger the safe operation of the power grid. In order to accurately identify foreign objects on power transmission lines, this paper proposes an unsupervised foreign object detection algorithm based on GMM (Gaussian Mixture Model) and k-means. Firstly, K-means is used for clustering, and then GMM is used for clustering. Finally, the foreign objects on the power transmission line are identified according to the clustering results. Experimental results show that the proposed algorithm has a high recognition rate. In addition, the more samples, the higher the recognition accuracy.

A Novel Foreign Object Detection Algorithm Based on GMM and K-Means for Power Transmission Line Inspection