This research proposes an efficient strip steel surface defect classification model (ASNet) based on convolutional neural network (CNN), which can run in real time on commonly used serial computing platforms. We only used a very shallow CNN structure to extract features of the defect images, and an attention layer which makes the model ignore some irrelevant noise and obtain an effective description of the defects is designed. In addition, a nonlinear perceptron is added to the top of the model to recognize defects based on the extracted features. On the strip steel surface defect image dataset NEU-CLS, our model achieves an average classification accuracy of 99.9 %, while the number of parameters of the model is only 0.041M and the computational complexity of the model is 98.1M FLOPs. It can meet the requirements of real-time operation and large-scale deployment on a common serial computing platform with high recognition accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.