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.

为了解决风力发电机叶片损伤的健康和生态问题，本文提出了一种新的基于 YOLOv5 的鸟类检测模型，该模型使用了 CBAM 注意机制和 RetinexNet 网络来提高网络性能，实验结果表明，该模型检测鸟类的准确度可以达到 87.40％，且该模型可以在夜晚，雨天和震动环境下表现出强的稳定性，证明了该模型能够确保风力发电机的安全稳定运行。

基于改进深度学习模型的海上风电场鸟类快速识别

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

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a
number of years in subsurface detection of explosive hazards. While WEMI
sensors have proven effective at localizing objects exhibiting large magnetic
responses, detecting objects lacking or containing very low amounts of
conductive materials can be challenging. In this paper, we compare a number of
target detection algorithms in the literature in terms of detection
performance. In the comparison, methods are tested on two real-world data sets:
one containing relatively low amounts of ground noise pollution, and the other
demonstrating highly-magnetic soil interference. Results are quantitatively
evaluated through receiver-operator characteristic (ROC) curves and are used to
highlight the strengths and weaknesses of the compared approaches in hand-held
explosive hazard detection.

本文采用接收器操作特征（ROC）曲线比较了文献中几种目标检测算法的检测性能，通过两个真实数据集的测试证明了该方法在手持式爆炸危险品检测中的优劣势，特别是在存在高磁性土壤干扰的情况下，Wide-band Electromagnetic Induction Sensors 在探测缺乏或含有非常少量导电材料的目标上仍存在挑战。