A Two-stage Focal Transformer for Human-Robot Collaboration-based Surface Defect Inspection

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Yiping Gao, Liang Gao, Xinyu Li
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引用次数: 0

Abstract

Human-robot collaboration has become a hotspot in smart manufacturing, and it also has shown the potential for surface defect inspection. The robot can release workload, while human collaboration can help to recheck the uncertain defects. However, the human-robot collaboration-based defect inspection can be hardly realized unless some bottlenecks have been solved, and one of them is that the current methods cannot decide which samples to be rechecked, and the workers can only recheck all of the samples to improve inspection results. To overcome this problem and realize the human-robot collaboration-based surface defect inspection, a two-stage Transformer model with focal loss is proposed. The proposed method divides the traditional inspection process into detection and recognition, designs a collaboration rule to allow workers to collaborate and recheck the defects, and introduces the focal loss into the model to improve the recognition results. With these improvements, the proposed method can collaborate with workers by rechecking the defects, and improve surface quality. The experimental results on the public dataset have shown the effectiveness of the proposed method, the accuracies are significantly improved by the human collaboration, which are 1.70%~4.18%. Moreover, the proposed method has been implemented into a human-robot collaboration-based prototype to inspect the carton surface defects, and the results also verify the effectiveness. Meanwhile, the proposed method has a good ability for visualization to find the defect area, and it is also conducive to defect analysis and rechecking.
基于人机协同的表面缺陷检测的两级焦点变换器
人机协作已成为智能制造领域的一个热点,也显示出表面缺陷检测的潜力。机器人可以释放工作量,而人类协作可以帮助重新检查不确定的缺陷。然而,除非解决了一些瓶颈,否则基于人机协作的缺陷检测很难实现,其中之一是目前的方法无法决定对哪些样本进行复查,工人只能对所有样本进行复查以提高检测结果。为了克服这一问题,实现基于人机协同的表面缺陷检测,提出了一种具有焦点损耗的两阶段变压器模型。该方法将传统的检测过程划分为检测和识别,设计了一个协作规则,允许工人协作并重新检查缺陷,并将焦点损失引入模型中以提高识别结果。有了这些改进,所提出的方法可以通过重新检查缺陷与工人合作,提高表面质量。在公共数据集上的实验结果表明了该方法的有效性,人工协作显著提高了方法的准确率,准确率为1.70%~4.18%。此外,该方法已应用于基于人机协作的纸箱表面缺陷检测原型中,结果也验证了其有效性。同时,该方法具有良好的可视化查找缺陷区域的能力,也有利于缺陷分析和复查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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