This paper discusses the challenges of real-time and robustness and safety protection faced by the application of visual detection in intelligent manufacturing

and puts forward the corresponding optimization countermeasures. The importance of model optimization and acceleration

environmental adaptability enhancement

and the hardware adaptation strategy are emphasized in real-time and robustness. By adopting model compression and hardware acceleration

the real-time detection capability of deep learning model can be improved; the adaptive algorithm can improve the robustness of the model to environmental change; and the deployment scheme for different hardware platforms can ensure the performance and compatibility of the model. In terms of security protection

the paper proposes the mechanisms of data encryption and anonymous processing

model security reinforcement and compliance management. By implementing these mechanisms

we can effectively protect the security of visual detection data

enhance the resistance of deep learning models to attacks

and ensure the legitimacy and compliance of the technology.