Smart Speed Enforcement using YOLOv8, Super-Resolution, and Exponential Penalty Model

This study investigates the application of deep learning in traffic enforcement, with a focus on speed enforcement and violation detection. The objective is to develop a real-time, efficient system for sustainable smart city traffic management. The velocity enforcement system combines YOLOv8 for vehicle detection, EDSR for high-definition image enhancement, and an exponential fine calculation process. Speed is assessed by tracking moving objects between frames using pre-defined speed assessment markers. The system runs at a rate of 23 FPS on an NVIDIA RTX 3090 GPU, enabling real-time applications and achieving higher precision. Image clarity benefits from EDSR super-resolution enhancement, which helps license plate recognition systems perform more effectively. Over-speeding vehicles receive their fines from an automatic system enabled by an exponential penalty model. We have confirmed the initial system that connects AI detection for traffic infractions with super-resolution image processing and exponential offence fining into a single automated framework for sustainable smart city traffic management systems.