Fast Recognition of birds in offshore wind farms based on an improved deep learning model

TL;DR

This paper presents an improved deep learning model integrating attention mechanisms and image enhancement techniques to accurately detect birds near offshore wind turbines, especially in low-light conditions, enhancing safety and ecological protection.

Contribution

The study introduces a novel combination of CBAM attention, RetinexNet enhancement, and optimized YOLOv5 for robust bird detection in challenging offshore environments.

Findings

Detection accuracy reached 87.40%.

Model performs well in night, rainy, and shaky conditions.

Improved detection stability and real-time performance.

Abstract

The safety of wind turbines is a prerequisite for the stable operation of offshore wind farms. However, bird damage poses a direct threat to the safe operation of wind turbines and wind turbine blades. In addition, millions of birds are killed by wind turbines every year. In order to protect the ecological environment and maintain the safe operation of offshore wind turbines, and to address the problem of the low detection capability of current target detection algorithms in low-light environments such as at night, this paper proposes a method to improve the network performance by integrating the CBAM attention mechanism and the RetinexNet network into YOLOv5. First, the training set images are fed into the YOLOv5 network with integrated CBAM attention module for training, and the optimal weight model is stored. Then, low-light images are enhanced and denoised using Decom-Net and Enhance-Net, and the accuracy is tested on the optimal weight model. In addition, the k-means++ clustering algorithm is used to optimise the anchor box selection method, which solves the problem of unstable initial centroids and achieves better clustering results. Experimental results show that the accuracy of this model in bird detection tasks can reach 87.40%, an increase of 21.25%. The model can detect birds near wind turbines in real time and shows strong stability in night, rainy and shaky conditions, proving that the model can ensure the safe and stable operation of wind turbines.