Strip Steel Surface Defect Recognition Based on LIR and GFNet
To address the problems of large computational cost and poor real-time performance of deep learning(DL)models in strip surface defect image recognition,a strip surface defect recognition method based on learnable image resizer(LIR)and glance and focus network(GFNet)is proposed.First,to ad-dress the problem of spatial redundancy of DL model in processing strip steel surface defect images,a GF-Net-driven strip steel surface defect recognition model is proposed,which can adaptively allocate computa-tional resources according to different samples and significantly reduce the computational effort in the mod-el inference stage;then,a joint training method of LIR and GFNet is proposed to realize feature enhance-ment for the recognition model while adjusting the image size Finally,a dataset of strip steel surface defects in a cold rolled sheet mill of an iron and steel enterprise is collected and analyzed using the proposed meth-od.Using the ResNet-50 model of residual networks(ResNet)as the backbone network and comparing it with the original ResNet-50,the proposed method reduces the inference time of a single image by about 3.58 times and the computational effort by about 6.11 times without sacrificing the accuracy,thus valida-ting the effectiveness of the proposed method.
NETL
NSTL
万方数据
维普
