Research on Recognition Method of Scratch on Surface Based on Convolution Neural Network
[Purposes]In view of the fact that the traditional manual visual inspection method cannot meet the needs of fast,accurate and automatic scratch detection on metal surfaces.Therefore,based on the con-struction of data sets and the convolutional neural network models,a method for scratch recognition of complex shape surfaces is proposed.[Methods]First,a data set of metal surface scratches was created;then,a convolutional neural network model based on Yolov8n is designed and trained.The model in-cludes backbone network,head network and neck network,which can meet the recognition requirements of different scratches.[Findings]After the training of the model,the F 1 curve was optimal in the inter-val of 0.3～0.5,which indicated that the model had good generalization ability in dealing with various scratches.Through the analysis of PR curve,when the accuracy rate is 0.65,and the recall rate is 0.8,the prediction effect of the model is the best.[Conclusions]The model optimization provides effective technical support for the automatic detection and recognition of metal surface scratches,and has practi-cal application value.
metal surface scratchtarget detectionYOLO algorithmdataset
NETL
NSTL
万方数据
