Implementation of Intelligent Video Monitoring System for Gas Pipelines Based on Improved YOLOv8
An improved YOLOv8 algorithm,integrating the CBAM attention mechanism and a 4x down-sampling detection head,is proposed to tackle the prevalent challenge of limited capability in identifying intricate and diverse small and multi-scale targets in surveillance videos along gas pipelines.Generally speaking,the experimental results show that the improved algorithm has achieved respective performance index of 80.2％for accuracy,72.8％for recall rate,and 80.2％for mean average accuracy mAP@0.5,which are 1.9％,3.8％,and 1.6％higher than the original YOLOv8s algorithm respectively.To be spe-cific,the efficiency,accuracy,and timeliness of this system in monitoring the safety of gas pipelines have been validated through a case study on safety supervision of gas pipelines in a specific city,which serves as a certain technical basis for achieving the normalization,standardization and refinement of gas pipeline safety supervision.
gas pipelinesvideo monitoringYOLOv8object detection
万方数据
维普
