Image recognition technology and applications based on deep neural networks

To enhance the accuracy and real-time performance of image recognition, this study explores deep neural network technologies and system design methods. Improvements in Convolutional Neural Networks (CNNs), attention mechanisms, and Focal Loss techniques enhance feature extraction and recognition performance. The system architecture, comprising data, model, service, and application layers, supports multi-source data access, edge deployment, and efficient processing using Redis and MongoDB. In industrial quality inspection, the system demonstrates high accuracy, low false detection rates, and fast processing in complex environments. Experimental results highlight superior recognition accuracy, resource efficiency, and adaptability compared to traditional methods, showcasing robustness and portability. This research confirms the potential of deep learning for industrial image recognition, supporting intelligent applications across various scenarios.