For the problems of low accuracy and poor environmental adaptability of traditional detection methods of Ringelmann emittance of exhaust,this paper proposes a detection method of Ringelmann emittance of exhaust based on improved YOLOv5s algorithm.Considering the variable shape and com-plex background of exhaust,adaptively spatial feature fusion(ASFF)and global attention mechanism(GAM)are added to the existing YOLOv5s network to improve the detection accuracy of exhaust tar-gets.At the same time,in order to reduce the impact of environmental factors such as illumination on exhaust target detection,based on the high temperature characteristics of exhaust,infrared images are used to improve the accuracy of exhaust region detection.Based on the standard Ringelmann emit-tance,the Ringelmann Emittance level of exhaust in the detected area is determined.The experimen-tal results show that the detection accuracy of the improved YOLOv5s is as high as 95.3％,which is 3.4％higher than that of the existing YOLOv5s;the influence of illumination and other environmen-tal factors on the detection results of exhaust targets is reduced,and the robustness of the algorithm is improved;the final determination accuracy of Ringelmann emittance of exhaust can reach level 0.5,which can effectively meet the high-precision detection requirements of Ringelmann emittance of exist-ing mobile source exhaust.
维普
万方数据