A Surface Defect Inspection Model via Rich Feature Extraction and Residual-Based Progressive Integration CNN
A Surface Defect Inspection Model via Rich Feature Extraction and Residual-Based Progressive Integration CNN
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基于丰富特征提取和残差累进积分的表面缺陷检测模型CNN
DOI：
10.3390/machines11010124
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发表时间：
2023-01
期刊：
影响因子：
2.6
通讯作者：
Guizhong Fu;Wenwu Le;Zengguang Zhang;Jinbin Li;Qixin Zhu;Fuzhou Niu;Hao Chen;Fangyuan Sun;Yehu Shen
中科院分区：
文献类型：
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作者：
Guizhong Fu;Wenwu Le;Zengguang Zhang;Jinbin Li;Qixin Zhu;Fuzhou Niu;Hao Chen;Fangyuan Sun;Yehu Shen
表面缺陷检测对产品质量控制和设备故障诊断具有重要意义。由于一些制造厂的自动化水平较低，而且很难识别缺陷，缺陷检查仍然具有挑战性。为了提高缺陷检测的自动化和智能化水平，结合工厂的实际需求，提出了一种USB组件高精度缺陷检测的细胞神经网络模型。首先，建立了缺陷检测系统，建立了包含凹坑、划痕、斑点、污渍和正常五类缺陷的USB-SG数据集。像素级缺陷地面真实注释被手动标记。本文提出了一种用于解决缺陷检测任务问题的CNN模型，并提出了三种提高模型性能的策略。该模型基于轻量级SqueezeNet网络构建，并设计了丰富的特征提取块来获取语义和细节信息。提出了基于残差的渐进特征融合方法，降低了模型微调的难度，提高了泛化能力。最后，提出了一种多步深度监督方案对特征集成过程进行监督。在USB-SG数据集上的实验表明，本文提出的模型性能优于其他方法，运行速度能够满足实时要求，在工业检测场景中具有广阔的应用前景。
Surface defect inspection is vital for the quality control of products and the fault diagnosis of equipment. Defect inspection remains challenging due to the low level of automation in some manufacturing plants and the difficulty in identifying defects. To improve the automation and intelligence levels of defect inspection, a CNN model is proposed for the high-precision defect inspection of USB components in the actual demands of factories. First, the defect inspection system was built, and a dataset named USB-SG, which contained five types of defects—dents, scratches, spots, stains, and normal—was established. The pixel-level defect ground-truth annotations were manually marked. This paper puts forward a CNN model for solving the problem of defect inspection tasks, and three strategies are proposed to improve the model’s performance. The proposed model is built based on the lightweight SqueezeNet network, and a rich feature extraction block is designed to capture semantic and detailed information. Residual-based progressive feature integration is proposed to fuse the extracted features, which can reduce the difficulty of model fine-tuning and improve the generalization ability. Finally, a multi-step deep supervision scheme is proposed to supervise the feature integration process. The experiments on the USB-SG dataset prove that the model proposed in this paper has better performance than that of other methods, and the running speed can meet the real-time demand, which has broad application prospects in the industrial inspection scene.