Enhanced Convolutional Neural Network for Image Processing in 6G Smart City Environments

Within the Beyond 6th Generation (B6G) network, this research presents an efficient Convolutional Neural Network (CNN) architecture specifically tailored for image processing in intelligent city applications. The suggested CNN model takes advantage of the very low-latency, high-speed nature of B6G networks to enable crowd-monitoring-based public-safety image analysis in real-time. By catering to the distributed nature of data processing and the reduced load on central servers, our architecture is well-suited to the B6G infrastructures’ edge computing environment. The CNN model accomplishes great accuracy in picture identification tasks with minimum processing overhead by utilising lightweight Convolutional layers and advanced optimisation techniques. We prove the model’s efficacy in real-time processing and analysis of high-resolution images from security cameras and drones in trials conducted in virtual smart city environments. The findings show that B6G networks and sophisticated image processing methods can work together to make smart city apps more efficient and responsive.