Research On Bird Nest Detection Method of Transmission Line Based on Yolov8
DOI:
https://doi.org/10.62051/ijcsit.v3n2.36Keywords:
YOLOv8, Deep learning, NestAbstract
In this study, we propose a YOLOv8-based method for detecting bird nests on transmission lines, aiming to solve the safety hazards caused by transmission lines being affected by nesting birds. First, we collect a large-scale image dataset containing transmission lines and label the bird nests in it. Then, we used YOLOv8 as a target detection model to enable accurate detection of bird nests in transmission lines by end-to-end training on the dataset. During the training process, we adopt Bounding Box Loss, Vision Feature Loss, and Classification Loss loss functions to help the model learn more accurate and meaningful feature representations and improve the detection performance. The experimental results show that our proposed method achieves high accuracy and recall on the bird nest detection task, which can effectively help the transmission line maintenance personnel to detect and deal with bird nests in time, and ensure the safe and stable operation of transmission lines.
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