China Telecom Corporation Limited Research Institute, Beijing, 102209, China.
Math Biosci Eng. 2022 Jun 17;19(9):8786-8803. doi: 10.3934/mbe.2022408.
The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the defects exhibit low contrast, different textures and geometric structures, and several defects making the surface defect detection more difficult. In this paper, a pixel-wise detection framework based on convolutional neural network (CNN) for strip steel surface defect detection is proposed. First we extract the salient features by a pre-trained backbone network. Secondly, contextual weighting module, with different convolutional kernels, is used to extract multi-scale context features to achieve overall defect perception. Finally, the cross integrate is employed to make the full use of these context information and decoded the information to realize feature information complementation. The experimental results of this study demonstrate that the proposed method outperforms against the previous state-of-the-art methods on strip steel surface defect dataset (MAE: 0.0396; : 0.8485).
自动表面缺陷检测系统通过减少信息并突出关键缺陷区域来支持高级图像理解的实时表面缺陷检测。然而，缺陷的对比度低、纹理和几何结构不同，并且存在多个缺陷，使得表面缺陷检测更加困难。本文提出了一种基于卷积神经网络（CNN）的用于带钢表面缺陷检测的像素级检测框架。首先，我们通过预训练的骨干网络提取显著特征。其次，使用具有不同卷积核的上下文加权模块提取多尺度上下文特征，以实现整体缺陷感知。最后，交叉积分用于充分利用这些上下文信息，并对信息进行解码，以实现特征信息的补充。该研究的实验结果表明，该方法在带钢表面缺陷数据集上的表现优于先前的最先进方法（MAE：0.0396；：0.8485）。
