Combining Vision Transformer and Attention Mechanism for Bird Nest Detection on Power Transmission Line

Ensuring the safe operation of power transmission lines amidst ecological concerns necessitates effective bird nest detection. Conventional UAV methods often struggle with accuracy, particularly in identifying nests against complex backgrounds. This paper introduces an enhanced avian nest recognition model tailored for transmission lines. Integrating YOLOv5 with Vision Transformer (ViT) and attention mechanisms, our approach leverages EfficientViT for robust feature extraction. A Context Aggregation mechanism adapts weights to focus on intricate targets, while decoupled heads in the prediction network minimize task interference. Experiments conducted on a dedicated dataset demonstrate significant advancements in detecting complex nest configurations. Compared to baseline methods, our model achieves a 22.5% increase in accuracy and a 19% higher average precision. Culminating in an overall mean average precision of 86.5% , showcasing substantial progress. This research underscores the effectiveness of our proposed methodology in enhancing bird nest detection on transmission lines, offering valuable insights for improving UAV inspection capabilities in challenging environments.