基于U-Net判别器的轮胎图像缺陷检测方法研究
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沈阳理工大学自动化与电气工程学院 沈阳 110159

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TN1

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辽宁省教育厅面上项目(LJKMZ2022061)资助


Research on tire image defect detection method based on U-Net discriminator
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School of Automation and Electrical Engineering, Shenyang University of Science and Technology,Shenyang 110159, China

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    摘要:

    轮胎缺陷检测对轮胎安全性能的鉴定有着重要意义,研究高性能的轮胎异常检测方法对汽车的安全性能极为重要。本文以生成对抗网络为基础提出一种基于U-Net判别器的网络模型UDGANomaly,首先在判别器中引入编码与解码,编码器模块执行逐图像分类,解码器模块输出逐像素分类决策,向生成器提供空间相干反馈。其次在生成器的编码器和解码器中引入自注意力机制,进一步关注多尺度特征中包含的代表性信息。最后设计了一种改进的基于结构相似性的生成器损失函数来解决视觉上的不一致性,从而提高不规则纹理检测的鲁棒性。经过对比研究发现本文提出的网络结构在同样的轮胎数据集上异常检测性能明显优于其他传统网络模型,并且平均测试精准度高达95.6%。

    Abstract:

    Tire defect detection is of great significance for the identification of tire safety performance, and researching high-performance tire anomaly detection methods is extremely important for the safety performance of automobiles. This article proposes a network model, UDGANomaly, based on U-Net discriminators, which is based on generative adversarial networks. Firstly, encoding and decoding are introduced into the discriminator. The encoder module performs image-by-image classification, and the decoder module outputs pixel-by-pixel classification decisions, providing spatially coherent feedback to the generator. Secondly, a self-attention mechanism is introduced in the encoder and decoder of the generator to further focus on the representative information contained in multi-scale features. Finally, an improved generator loss function based on structural similarity was designed to address visual inconsistency and enhance the robustness of irregular texture detection. After comparative research, it was found that the network structure proposed in this paper has significantly better anomaly detection performance than other traditional network models on the same tire dataset, and the average testing accuracy is as high as 95.6%.

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张兴伟,刘韵婷,陈浩,丁海峰.基于U-Net判别器的轮胎图像缺陷检测方法研究[J].电子测量技术,2024,47(16):139-146

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  • 在线发布日期: 2024-12-10
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