产品表面缺陷检测是工业自动化生产的重要环节，准确率是评价自动检测系统可靠性的主要指标。基于复杂纹理表面缺陷检测的特殊性以及对检测方法的实时性、通用性等要求，提出了优化骨干网络并使用迁移学习特征映射构建复杂纹理表面缺陷的检测方法。该方法通过优化残差网络模型并建立仿真数据集的方式进行迁移学习，以解决实际情况中复杂纹理表面产品数据集样本数量少、数据集制作困难、相似问题难以互相兼容等问题。实验结果表明，提出的方法可以准确地检测随机复杂纹理的人造木质板材表面缺陷，平均准确率可达99.6%。现有实验条件下单张人造木质板材的检测时间为305 ms，可以满足在线检测的实时性要求。研究结果可为基于深度学习的复杂纹理表面缺陷检测提供新的思路与理论参考。
Abstract
Defect detection of product surface is an important part of industrial automatic production and the accuracy is the main index to evaluate the reliability of automatic detection system. Based on the particularity of defect detection on complex texture surface and the requirements of real-time and universal detection methods, a detection method for optimizing the backbone network and using the transfer learning feature mapping to construct the complex texture surface defects was proposed. In this method, the ResNet model was optimized and the simulation data set was established for transfer learning, so as to solve the problems such as the small number of samples in the data set of complex texture surface products, the difficulty of data set making, and the difficulty of similar problems to be compatible with each other. Experimental results show that the proposed method can accurately detect the surface defects of artificial wooden plank with random complex texture, and the average accuracy can reach 99.6%. Under the existing experimental conditions, the detection time of a single artificial wooden plank is 305 ms, which can meet the real-time requirements of online detection. The research results can provide a new idea and theoretical reference for the defect detection on complex texture surface based on deep learning.
关键词
复杂纹理 /
残差网络 /
迁移学习 /
缺陷检测 /
机器视觉
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Key words
machine vision /
complex texture /
transfer learning /
ResNet /
defect detection
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基金
国家自然科学基金（62002254，61876121）江苏省自然科学基金（BK20200988）
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