摘要
针对传统方式检测风力涡轮机表面缺陷时出现的精度不足、泛化性较差问题,提出了一种改进YOLOv5s的风力涡轮机表面缺陷检测模型。在网络结构方面,首先在主干特征提取网络引入改进的MobileNetv3网络,用于协调并平衡模型的轻量化和精度关系;其次采用BiFPN式的融合方式,增强神经网络的多尺度适应能力,提高融合速度和效率;最后为轻量化的自适应调节特征权重,运用ECAnet通道注意力机制,进一步提高神经网络的特征提取能力。在损失函数方面,将边框回归的损失函数修改为αIoU Loss,进一步提升了bbox回归精度。实验结果表明,基于YOLOv5s的改进算法可以在复杂环境下快速准确地识别风机表面的缺陷目标,能够满足实时目标检测的实际应用需求。
Aiming at the problem of insufficient precision and poor generalization in the traditional way of wind turbine surface defect detection, an improved YOLOv5s wind turbine surface defect detection model is proposed. In terms of network structure, an improved MobileNetv3 network is introduced into the backbone feature extraction network to coordinate and balance the lightweight and accuracy relationship of the model. Secondly, the BiFPN fusion method is adopted to enhance the multi-scale adaptability of the neural network and improve the fusion speed and efficiency. Finally, for the lightweight adaptive adjustment of feature weights, the ECAnet channel attention mechanism is used to further improve the feature extraction ability of the neural network. In terms of loss function, the loss function of bounding box regression is modified to αIoU Loss, which further improves the accuracy of bbox regression. The experimental results show that the improved algorithm based on YOLOv5s can quickly and accurately identify the defect targets on the surface of the wind turbine in complex environments, and can meet the practical application requirements of real-time target detection.
作者
张银胜
杨宇龙
吉茹
蓝天鹤
单慧琳
Zhang Yinsheng;Yang Yulong;Ji Ru;Lan Tianhe;Shan Huilin(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Electronic and Information Engineering,Wuxi University,Wuxi 214105,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第1期40-49,共10页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(62071240,62106111)
江苏省一流本科课程《电路分析基础》无锡学教学改革重点课题(JGZD202109)项目资助
