Integration of Image Retrieval and Machine Learning in Smart Manufacturing

This paper proposes an innovative framework that combines convolutional neural networks (CNNs) with transfer learning to address the limitations of traditional image retrieval methods in dynamic industrial environments in terms of efficiency and accuracy. By leveraging automatic feature learning and model optimization, the framework enhances both the efficiency and accuracy of image processing. Experimental results demonstrate significant advantages in production efficiency and product quality, showcasing the potential application of this method in smart manufacturing. This research provides theoretical support and practical reference for the future development of intelligent manufacturing technologies.