A steel surface defect inspection approach towards smart industrial monitoring
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A steel surface defect inspection approach towards smart industrial monitoring Ruiyang Hao1 · Bingyu Lu1 · Ying Cheng2 · Xiu Li3 · Biqing Huang1 Received: 30 May 2020 / Accepted: 14 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely used in manufacturing equipment, and steel surface defect inspection is of great significance to the normal operation of steel equipment in manufacturing workshops. In steel defect inspection systems, industrial inspection robots generate images via scanning steel surface, and processors perform surface defect inspection algorithms on images. We focus on applying advanced object detection techniques to surface defect inspection algorithm for sheet steel. In the proposed steel surface defect inspection model, a deformable convolution enhanced backbone network firstly extracts complex features from multi-shape steel surface defects. Then the feature fusion network with balanced feature pyramid generates high-quality multi-resolution feature maps for the inspection of multi-size defects. Finally, detector network achieves the localization and classification of steel surface defects. The proposed model is evaluated on a typical steel surface defect dataset. Our model achieves 0.805 mAP, 0.144 higher than baseline models, and our model shows high efficiency in inference. Experiments are performed to reveal the effect of employed approaches, and results also show our model achieves a balance between inspection performance and inference efficiency. Keywords Defect inspection · Object detection · Smart industrial monitoring · Steel surface · Deep learning
Introduction With the advance in Industrial Internet of Things and artificial intelligence, the fourth industrial revolution, i.e. Industry
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Xiu Li [email protected] Biqing Huang [email protected] Ruiyang Hao [email protected] Bingyu Lu [email protected] Ying Cheng [email protected]
1
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
2
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
3
Division of Information Science, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
4.0, is rapidly developing (Cohen et al. 2019). As a typical application of Industry 4.0, the smart industrial monitoring system adopts ubiquitous sensors and processors to monitor equipment and timely discover faults, where artificial intelligence algorithms are employed to automatically inspect defects (Pimenov et al. 2018). Towards smart industrial monitoring, we propose a steel surface defect inspection model based on advanced object detection approaches, and the main application scenarios of our research focus on defect inspection of steel equipment w
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