摘要
针对太阳能电池片缺陷检测方法存在精度低的问题,提出一种基于改进的YOLOv5s太阳能电池片表面缺陷检测算法。首先,为了解决电池片小目标缺陷检测问题,提出了上下文Transformer网络(CoT),可以为小目标提供全局上下文信息,帮助模型更好地预测小目标。其次,将CBAM注意力加入到Head部分的C3模块,能够更好地捕捉输入特征图的重要通道和空间位置,提高模型的性能和鲁棒性。接着,使用轻量级的通用上采样算子CARAFE减少上采样过程中特征信息的损失,保证了特征信息的完整性。最后,使用WIoU作为边界框损失函数,大幅提升了回归的准确性,并且有助于快速实现模型的收敛。实验结果显示,改进后的YOLOv5s相较于原始算法在Precision、Recall、mAP@0.5三个指标上分别提高了5.5%、4.1%、3.3%,检测速度达到了76 FPS,满足太阳能电池片缺陷检测要求。
Aiming at the problem of low accuracy of the method for solar cell defect detection,a surface defect detection algorithm based on the improved YOLOv5s solar cell is proposed.First,in order to solve the problem of small target defect detection on the cell sheet,the Contextual Transformer Network(CoT)is proposed,which can provide global contextual information for small targets and the model better at predicting small targets.Secondly,by adding CBAM attention to the C3 module in the Head part,the important channels and spatial locations of the input feature maps can be better captured to improve the performance and robustness of the model.Next,the integrity of feature information is ensured by using CARAFE,a lightweight generalized up-sampling operator,to reduce the loss of feature information during up-sampling.Finally,by using WIoU as the bounding box loss function,the accuracy of the regression can be greatly improved and the convergence of model can be achieved quickly.The experimental results show that compared with the original algorithm,the improved YOLOv5s improves the three indicators of Precision,Recall,and mAP@0.5 by 5.5%,4.1%,and 3.3% respectively,and the detection speed reaches 76 FPS,which meets the requirements of solar cell defect detection.
作者
彭雪玲
林珊玲
林志贤
郭太良
PENG Xueling;LIN Shanling;LIN Zhixian;GUO Tailiang(School of Advanced Manufacturing,Fuzhou University,Quanzhou 362252,China;Fujian Science and Technology Innovation Laboratory for Photoelectric Information,Fuzhou 350116,China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2024年第2期237-247,共11页
Chinese Journal of Liquid Crystals and Displays
基金
国家重点研发计划(No.2021YFB3600603)
福建省自然科学基金(No.2020J01468)
国家自然科学基金青年科学基金(No.62101132)。
作者简介
彭雪玲,女,硕士研究生,2021年于景德镇陶瓷大学获得学士学位,主要从事图像处理方面的研究。E-mail:1733580405@qq.com;通信联系人:林珊玲,女,博士,讲师,2020年于福州大学获得博士学位,主要从事显示驱动、图像处理等方面的研究。E-mail:sllin@fzu.edu.cn。
