Abstract
Images captured from the real world are often affected by different types of noise, which can significantly impact the performance of Computer Vision systems and the quality of visual data. This study presents a novel approach for defect detection in casting product noisy images, specifically focusing on submersible pump impellers. The methodology involves utilizing deep learning models such as VGG16, InceptionV3, and other models in both the spatial and frequency domains to identify noise types and defect status. The research process begins with preprocessing images, followed by applying denoising techniques tailored to specific noise categories. The goal is to enhance the accuracy and robustness of defect detection by integrating noise detection and denoising into the classification pipeline. The study achieved remarkable results using VGG16 for noise type classification in the frequency domain, achieving an accuracy of over 99%. Removal of salt and pepper noise resulted in an average SSIM of 87.9, while Gaussian noise removal had an average SSIM of 64.0, and periodic noise removal yielded an average SSIM of 81.6. This comprehensive approach showcases the effectiveness of the deep AutoEncoder model and median filter, for denoising strategies in real-world industrial applications. Finally, our study reports significant improvements in binary classification accuracy for defect detection compared to previous methods. For the VGG16 classifier, accuracy increased from 94.6% to 97.0%, demonstrating the effectiveness of the proposed noise detection and denoising approach. Similarly, for the InceptionV3 classifier, accuracy improved from 84.7% to 90.0%, further validating the benefits of integrating noise analysis into the classification pipeline.
Abstract (translated)
现实世界中的图像通常受到各种类型的噪声的影响，这可能会显著影响计算机视觉系统和视觉数据的质量。这项研究提出了一种新的方法来检测铸件产品噪声图像中的缺陷，特别关注潜水泵叶轮。该方法涉及利用像VGG16、InceptionV3等这样的深度学习模型在空间和频域中识别噪声类型和缺陷状态。研究过程从预处理图像开始，然后应用特定噪声类别的去噪技术。通过将噪声检测和去噪整合到分类管道中，旨在提高缺陷检测的准确性和鲁棒性。使用VGG16在频域对噪声类型分类，获得了超过99%的准确率。消除盐和胡椒噪声平均SSIM为87.9，高斯噪声消除平均SSIM为64.0，周期性噪声消除平均SSIM为81.6。这种全面的方法突出了在现实工业应用中使用深度自编码器模型和均值滤波器进行去噪策略的有效性。最后，我们的研究报道了与以前方法相比，缺陷检测二分类准确率的重大改进。对于VGG16分类器，准确率从94.6%增加到97.0%，表明所提出的噪声检测和去噪方法的有效性。同样，对于InceptionV3分类器，准确率从84.7%增加到90.0%，进一步证实了将噪声分析整合到分类管道中的好处。
URL
https://arxiv.org/abs/2404.10664
