Recognition of bird nests on transmission lines based on YOLOv5 and DETR using small samples

Abstract

The safety and reliability of transmission line operations are critical to the delivery of electricity. Bird nests are one of the frequent potential factors affecting the safety of transmission lines. The main objective of this paper is to propose a small-sample learning method to detect bird nests on transmission lines. The method combines the two latest deep learning models, YOLOv5 and detection transformer (DETR). Inspired by biological vision, this method transfers the learning of bird nests in daily scenes to the recognition of bird nests on transmission lines. The proposed method is evaluated by two public datasets. The test on the first one presents a recognition rate of 95.50%, whereas the training set only contains ten homologous data and 80 non-homologous data. The second test shows that 85.54% of the samples are recognized by generalization without homologous training data. The results show that our method provides a way to identify bird nests on transmission lines with the help of bird nests of daily scenes under small sample conditions.

Keywords

• Energy Transportation
• Transmission line inspection
• Bird nest recognition
• YOLOv5
• Detection transformer
• Deep learning