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.

翻译：真实世界采集的图像常受多种噪声干扰，这会显著影响计算机视觉系统的性能与视觉数据质量。本研究提出一种面向铸件产品含噪图像的缺陷检测新方法，特别聚焦于潜水泵叶轮。该方法通过利用VGG16、InceptionV3等深度学习模型，在空间域与频域中联合识别噪声类型与缺陷状态。研究流程始于图像预处理，继而针对特定噪声类别应用去噪技术，旨在通过将噪声检测与去噪整合至分类管道中，提升缺陷检测的准确率与鲁棒性。本研究采用VGG16在频域进行噪声类型分类，取得了超过99%的准确率。去除椒盐噪声后平均结构相似性指数（SSIM）达到87.9，高斯噪声去除后平均SSIM为64.0，周期性噪声去除后平均SSIM为81.6。该综合方案展示了深度自编码器模型与中值滤波在工业实际应用去噪策略中的有效性。最终，本研究报告相较于先前方法，缺陷检测二分类准确率取得显著提升：VGG16分类器准确率从94.6%提高至97.0%，验证了所提噪声检测与去噪方法的有效性；InceptionV3分类器准确率从84.7%提升至90.0%，进一步证实了将噪声分析融入分类管道的优越性。