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A One-Stage Deep Learning Model for Industrial Defect Detection
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2023-05-07 , DOI: 10.1002/adts.202200853 Zhaoguo Li 1 , Xiumei Wei 1 , M. Hassaballah 2, 3 , Xuesong Jiang 1
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2023-05-07 , DOI: 10.1002/adts.202200853 Zhaoguo Li 1 , Xiumei Wei 1 , M. Hassaballah 2, 3 , Xuesong Jiang 1
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Industrial defect detection is a hot topic in the field of computer vision and industry. Industrial defects are diverse and complex, and well-known machine learning based methods can often not effectively extract features of industrial defects and achieve good detection results. To address the above problems, this paper introduces a deep learning model for industrial defect detection. First, a two-branch decoupled head, which can facilitate model training through separating the prediction of category and regression is designed. Also, two inverted bottleneck structures are designed to enhance the ability of the model to extract features. Moreover, an attention-enhanced feature fusion (AEFF) module is designed and integrated into the neck network to achieve effective feature fusion. Extensive experiments are conducted on three public datasets, namely the DeepPCB dataset, NEU-DET dataset, and NRSD-MN dataset. The obtained results demonstrate that the proposed model achieves competitive results compared to the state-of-the-art methods. The proposed model achieves mAP@0.5:0.95, 71.78%, 36.04%, and 48.69% on the PCB dataset, NEU-DET dataset, and the NRSD-MN dataset, respectively.
中文翻译：
一种用于工业缺陷检测的单阶段深度学习模型
工业缺陷检测是计算机视觉和工业领域的热门话题。工业缺陷多样且复杂，众所周知的基于机器学习的方法往往不能有效提取工业缺陷特征并取得良好的检测效果。针对上述问题，本文提出了一种工业缺陷检测的深度学习模型。首先，设计了一个两分支解耦头，它可以通过分离类别预测和回归来促进模型训练。此外，还设计了两个倒置瓶颈结构来增强模型提取特征的能力。此外，设计了注意力增强特征融合（AEFF）模块并将其集成到颈部网络中，以实现有效的特征融合。在三个公共数据集上进行了广泛的实验，分别是DeepPCB数据集、NEU-DET数据集和NRSD-MN数据集。获得的结果表明，与最先进的方法相比，所提出的模型取得了有竞争力的结果。所提出的模型在 PCB 数据集、NEU-DET 数据集和 NRSD-MN 数据集上分别实现了 mAP@0.5:0.95、71.78%、36.04% 和 48.69%。
更新日期：2023-05-07
中文翻译：
一种用于工业缺陷检测的单阶段深度学习模型
工业缺陷检测是计算机视觉和工业领域的热门话题。工业缺陷多样且复杂，众所周知的基于机器学习的方法往往不能有效提取工业缺陷特征并取得良好的检测效果。针对上述问题，本文提出了一种工业缺陷检测的深度学习模型。首先，设计了一个两分支解耦头，它可以通过分离类别预测和回归来促进模型训练。此外，还设计了两个倒置瓶颈结构来增强模型提取特征的能力。此外，设计了注意力增强特征融合（AEFF）模块并将其集成到颈部网络中，以实现有效的特征融合。在三个公共数据集上进行了广泛的实验，分别是DeepPCB数据集、NEU-DET数据集和NRSD-MN数据集。获得的结果表明，与最先进的方法相比，所提出的模型取得了有竞争力的结果。所提出的模型在 PCB 数据集、NEU-DET 数据集和 NRSD-MN 数据集上分别实现了 mAP@0.5:0.95、71.78%、36.04% 和 48.69%。
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