Abstract. Defect detection is critical for product quality assessment. Currently, machine vision technology has gradually replaced inefficient manual inspections. Due to the intricate textures and assorted defects on the product surface, conventional defect detection technology still requires ameliorating. We propose an improved deep learning network for defect detection of magnetic sheets, which contains the major contributions at two aspects. (a) Image preprocessing is utilized to enhance the defect features of the dataset. (b) With the superiorities of the core structures of various efficient convolutional neural networks, several perception modules are formulated for multiscale feature extraction and are stacked to construct our inspection network. The parameters are effectively reduced while pursuing the detection accuracy, which is more in line with industrial computing requirements. Experiments show that our defect detection on magnetic sheets has achieved great results, and the computational resources are saved. Moreover, a case extended to the defect detection of hot rolled steel indicates that the proposed network is scalable and has great application potential.

中文翻译：

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基于感知模块堆叠卷积神经网络的磁片缺陷检测

摘要。缺陷检测对于产品质量评估至关重要。目前，机器视觉技术已逐渐取代低效的人工检查。由于产品表面纹理复杂，缺陷种类繁多，传统的缺陷检测技术仍有待改进。我们提出了一种改进的用于磁片缺陷检测的深度学习网络，它包含两个方面的主要贡献。(a) 利用图像预处理来增强数据集的缺陷特征。(b) 利用各种高效卷积神经网络核心结构的优势，为多尺度特征提取制定了几个感知模块，并堆叠起来构建我们的检测网络。在追求检测精度的同时有效降低参数，更符合工业计算的要求。实验表明，我们对磁片的缺陷检测取得了很好的效果，并且节省了计算资源。此外，一个扩展到热轧钢缺陷检测的案例表明，所提出的网络是可扩展的，具有巨大的应用潜力。