太阳能电池片图像校正与表面缺陷检测  

Image calibration and surface defect detection of solar photovoltaic cells

作　　者：朱栋 胡伟笑 赵腾 Zhu Dong;Hu Weixiao;Zhao Teng(College of Microelectronics and Control Engineering,Changzhou University,Changzhou 213159,China)

机构地区：[1]常州大学微电子与控制工程学院,常州213159

出　　处：《电子测量技术》2024年第8期126-133,共8页Electronic Measurement Technology

基　　金：江苏省科技支撑项目(DFJH202131)资助。

摘　　要：针对太阳能电池片图像的透视失真与表面缺陷检测问题,提出一种基于虚拟相机的太阳能电池片图像的透视校正方法和改进YOLOv5s的神经网络模型。首先,根据相机外参构建水平姿态的虚拟相机,建立原图与虚拟相机的透视映射关系,以实现原图的透视校正。然后,采用动态头部来提高YOLOv5s头部的表示能力,并在C3模块的瓶颈处加入感受野增强模块RFI来提高小目标感受野。最后,将YOLOv5s的定位loss与NWD loss进行融合来弥补小目标位置偏差。实验结果表明,基于虚拟相机的透视校正,其效果明显优于传统方法且运行时间更短;同时改进后的YOLOv5s模型对比YOLOv5s、YOLOv7、YOLOv8平均精度分别提高6.1%、27.7%、1.1%,对太阳能电池片表面质量检测具有实际应用价值。Aiming at image perspective distortion and surface defect detection of solar photovoltaic cells,a method based on a virtual camera for perspective correction and an improved YOLOv5s neural network model for defect detection are proposed.Firstly,a virtual camera with a horizontal orientation is constructed based on camera extrinsics to establish a perspective mapping relationship between the original image and the virtual camera,by which perspective correction of the original image is achieved.Secondly,a dynamic head is employed to enhance the representation capacity of the YOLOv5s head,and a receptive field expansion(RFI) module is added into the bottleneck of the C3 module to enhance the receptive field for small targets.Finally,the localization loss of YOLOv5s is fused with the normalized weighted distance(NWD) loss to compensate for the positional deviation of small targets.Experimental results demonstrate that the perspective correction based on the virtual camera can achieve significant improvements in correction effectiveness with shorter runtime.Moreover,the average accuracy of the improved YOLOv5s model can be increased up to 6.1%,27.7%,and 1.1% than YOLOv5s,YOLOv7,and YOLOv8 respectively,which exhibits the practical value in surface quality inspection of solar photovoltaic cells.

关 键 词：太阳能电池片 透视校正 虚拟相机 YOLOv5s 缺陷检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]