Deep Learning and Visual Saliency-based Bird Nest Identification for Transmission Lines
Exploring automatic identification method for bird nest from drone inspection images is of great significance to safe operation of transmission lines.Therefore a bird nest identification method combining visual saliency and deep learning is proposed.First the saliency map is extracted by the visual saliency algorithm and fused with the visible image,so that the fused image has the advantages of both rich feature information and significant nest target.Then the fused images are input into the deep learning model for training.The model constructs a feature pyramid to meet the needs of multi-scale bird nest target identification.Finally the classification and regression sub-model are used to output the identification re-sults.The experimental results show that the proposed method can accurately recognize images with different back-grounds,tower types,shooting angles and shooting distances,and has good robustness and generalization.The Precision,Recall and IoU accuracy index values are 0.9765,0.9651 and 0.9579 respectively,superior to several popular deep learning models.
万方数据
维普
