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A steel surface defect inspection approach towards smart industrial monitoring
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2020-09-20 , DOI: 10.1007/s10845-020-01670-2
Ruiyang Hao , Bingyu Lu , Ying Cheng , Xiu Li , Biqing Huang

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.



中文翻译:

用于智能工业监控的钢表面缺陷检查方法

随着工业4.0的发展,已经提出了智能工业监控来及时发现工业过程中的故障和缺陷。钢铁广泛用于制造设备中,钢铁表面缺陷检查对制造车间中钢铁设备的正常运行具有重要意义。在钢缺陷检查系统中,工业检查机器人通过扫描钢表面生成图像,然后处理器对图像执行表面缺陷检查算法。我们致力于将先进的物体检测技术应用于钢板表面缺陷检测算法。在提出的钢表面缺陷检测模型中,首先采用可变形卷积增强主干网络从多形钢表面缺陷中提取复杂特征。然后,带有平衡特征金字塔的特征融合网络生成高质量的多分辨率特征图,用于检查多种尺寸的缺陷。最后,探测器网络实现了钢表面缺陷的定位和分类。建议的模型在典型的钢表面缺陷数据集上进行评估。我们的模型达到0.805 mAP,比基线模型高0.144,并且模型显示出很高的推理效率。进行实验以揭示所采用方法的效果,结果还表明我们的模型在检查性能和推理效率之间取得了平衡。建议的模型在典型的钢表面缺陷数据集上进行评估。我们的模型达到0.805 mAP,比基线模型高0.144,并且模型显示出很高的推理效率。进行实验以揭示所采用方法的效果,结果还表明我们的模型在检查性能和推理效率之间取得了平衡。建议的模型在典型的钢表面缺陷数据集上进行评估。我们的模型达到0.805 mAP,比基线模型高0.144,并且模型显示出很高的推理效率。进行实验以揭示所采用方法的效果,结果还表明我们的模型在检查性能和推理效率之间取得了平衡。

更新日期:2020-09-20
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