基于改进YOLOX-S的太阳能电池片表面缺陷检测

Surface defect detection of solar cells based on improved YOLOX-S

针对太阳能电池片表面缺陷检测存在模型体积大和检测性能不达标的问题,提出了一种轻量化YOLOX-S检测模型用于工业生产。首先以YOLOX-S模型为基础,采用轻量级网络MobileNetV3优化主干网络,减少模型参数,降低模型运算量,提高检测速度。其次采用FReLU激活函数改进MobileNetV3,使模型具有空间像素级建模能力,提高模型空间特征信息灵敏度,增强模型对小目标缺陷的特征提取能力。最后,在颈部网络引入注意力特征融合模块,聚合多尺度信息,加强模型的多尺度特征融合能力。实验结果表明,改进的YOLOX-S检测模型平均精度均值可达97.6%,参数量减少43.2%,检测速度达到51帧/s,置信度均在90%以上,检测结果可靠。

A lightweight YOLOX-S detection model is proposed for industrial production to address the issues of large model size and unsatisfactory detection performance in surface defect detection of solar cells.Firstly,based on the YOLOX-S model,the lightweight network MobileNetV3 is used to optimize the backbone network,reduce model parameters,reduce model computation,and improve detection speed.Secondly,MobileNetV3 is improved by using the FReLU Activation function to make the model have the spatial pixel level modeling ability,improve the sensitivity of model spatial feature information,and enhance the feature extraction ability of the model for small target defects.Finally,an attention feature fusion module is introduced into the neck network to aggregate multi-scale information and enhance the model's multi-scale feature fusion capability.The experimental results show that the average accuracy of the improved YOLOX-S detection model can reach 97.6%,the number of parameters can be reduced by 43.2%,the detection speed can reach 51 frames/s,and the confidence level is above 90%.The detection results are reliable.