Research on Carbon Emission Monitoring System Based on YOLO and Photoacoustic Sensing Technology

This paper proposes a carbon emission monitoring system that integrates machine vision and photoacoustic sensing technology. The system employs the YOLOv8 object detection algorithm to rapidly identify image features of industrial equipment and other potential emission sources, while edge detection techniques are used for precise location positioning. Additionally, photoacoustic sensors are utilized to monitor gas concentrations in the target area in real time, enabling a comprehensive assessment of emission source locations and emission intensity. The proposed method is highly adaptable to complex environments, demonstrating exceptional accuracy and robustness in industrial settings. By providing an intelligent and efficient solution for carbon emission monitoring, this system contributes to the advancement of low-carbon development and sustainable environmental management.