Steel Plate Surface Defect Detection Based on Improved MobileNetV2
There are many types of defects on the surface of steel plates,the disclosure rate of industry data is very low,and the lack of training samples makes it difficult for deep learning to be applied in this field.The feature representation capability of MobileNetV2 network model is limited and its robustness is weak.To solve the above problems,an improved MobileNetV2 network model is proposed,which can have a high accuracy in small-scale sample detection.Resetting the upper limit of the activation function in the network model allows the model to better capture complex patterns and features in the input data.A new bottleneck structure is proposed and the number of network layers is reduced,which can integrate the feature graph in channel dimension and improve the representation and feature extraction ability of the model.Enhance fea-ture recognition,extract more comprehensive and distinctive features,and enhance the accuracy and robust-ness of the model.Experimental results demonstrate that the improved MobileNetV2 network model a-chieves an accuracy of 98.7%,surpassing the original network as well as other convolutional neural net-works.This improved model effectively detects surface defects in small steel plate samples.
万方数据
维普
