Abstract The rapid evolution of computer vision (CV) and networking technologies has led to the emergence of intelligent, data-driven communication systems capable of real-time perception and decision-making. This paper explores the integration of computer vision with modern networking infrastructures and examines how this convergence enhances network efficiency, security, and scalability. Recent advances in deep learning–based vision models, combined with high-speed 5G networks and edge computing, enable near real-time visual analytics with anomaly detection accuracy reaching up to 98%. Key application domains discussed include telecommunication infrastructure maintenance, IoT-enabled smart systems, and network-based security and surveillance. Performance evaluation demonstrates that edge-assisted CV architectures significantly outperform cloud-only systems by reducing latency to below 10 ms and lowering bandwidth consumption by more than 60%. Despite these benefits, challenges remain related to computational overhead, privacy preservation, data security, and ethical concerns such as algorithmic bias. The paper also highlights emerging trends toward AI-native 6G networks, multi-modal perception systems, and ethical AI frameworks designed to ensure fairness and regulatory compliance. Overall, the study emphasizes that computer vision will play a critical role in the development of future smart networks, provided that technical and ethical challenges are addressed through optimized architectures and responsible AI practices
Namrata Vishal Paygude (Sat,) studied this question.