激光与光电子学进展
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2025, 62 (2)
: 0212007, 网络出版: 2025-01-07
无监督领域自适应的管状容器表面缺陷检测
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Defect Detection of Tubular Containers Based on an Unsupervised Domain Adaptive Algorithm
缺陷检测
无监督领域自适应
管状容器
神经网络
defect detection
unsupervised domain adaptation
tubular containers
neural network
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本文提出了一种无监督领域自适应的表面缺陷检测算法，针对管状容器表面缺陷图像易受环境因素影响的问题，通过卷积神经网络提取特征，采用域分类器对抗训练策略进行特征对齐，并改进通道注意力融合域分类器，提高了检测精度，减少了错检漏检，增强了算法对实际生产环境的适应性。
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摘要
采集时管状容器表面缺陷图像，图像容易因环境因素发生变化，导致采集图像特征与算法训练图像特征不一致。为了解决由此引起的检测精度下降问题，提出一种无监督领域自适应的表面缺陷检测算法。首先，利用卷积神经网络提取源域中有标签数据和目标域中无标签数据；其次，采用域分类器对抗训练策略进行图像级特征和实例级特征对齐，为充分利用不同尺度特征图的关联性，提出改进通道注意力融合域分类器来增强域分类器的鉴别能力；最后，将对应域分类器的分类结果进行强匹配，促使网络检测结果与输入数据来源无关，生成领域自适应的域不变检测，以此提高检测精度。实验结果表明，所提算法模型检测精度从83.1%提升到了93.4%，显著减少了错检、漏检现象，所提算法对实际生产易变环境有更强的适应性。
Abstract
When images of surface defects on tubular vessels are acquired, the images are prone to change due to environmentally variable factors, resulting in inconsistency between the collected image features and the algorithm’s training image features. To solve this problem of degradation in detection accuracy, in this study, an unsupervised domain adaptive surface defect detection algorithm is proposed. First, the convolutional neural network extracts the labeled data in the source domain and unlabeled data in the target domain. Second, the strategy of adversarial training of domain classifiers is used to align image-level features and instance-level features. To fully utilize the correlation of feature maps at different scales, an improved channel-attention fusion domain classifier is proposed to enhance the discriminative ability of the domain classifiers. Finally, the results of the corresponding domain classifiers are strongly matched to ensure that the network detection results are independent of input data source. Specifically, the detection is conducted under the condition of the generated domain adaptive domain invariant to enhance the detection accuracy. The experimental results show that the detection accuracy of the algorithm model is improved from 83.1% to 93.4%, which significantly reduces the phenomenon of wrong detection and missed detection, and the algorithm is more adaptable to the variable environment of the actual production.
张广志, 李慧敏, 宋旭宁. 无监督领域自适应的管状容器表面缺陷检测[J]. 激光与光电子学进展, 2025, 62(2): 0212007. Guangzhi Zhang, Huimin Li, Xuning Song. Defect Detection of Tubular Containers Based on an Unsupervised Domain Adaptive Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0212007.