Research on Mask R-CNN-based pointer instrument recognition method for oilfield wellsite
Aiming at the problems of fuzzy wellsite instrumentation images captured during the UAV inspection process and poor real-time oilfield instrumentation localization model,an improved maximum a posteriori probability model deblurring method and a pointer instrumentation localization algorithm based on Mask R-CNN are proposed.Firstly,the a priori information of the image is optimized by using a var-iable step size LMS filter,and the filter parameters are adjusted according to the statistical characteristics of the input data to generate the preliminary instrument image recovery results,so as to improve the de-blurring effect of the maximum a posteriori probability.Secondly,on the basis of the Mask R-CNN net-work structure,MobileNetV3 is selected as the main feature extraction network to reduce the number of parameters,and the attention mechanism module is added to ensure the accuracy of the Mask R-CNN network structure.Secondly,based on the Mask R-CNN network structure,MobileNetV3 is chosen as the backbone feature extraction network to reduce the number of parameters,and then the attention mech-anism module is added to ensure the accuracy to complete the instrument positioning.Finally,the experi-ment proves that the evaluation index of instrument image is higher than other algorithms,and the instru-ment localization algorithm proposed in this paper reduces the number of parameters by 48.25 M,and the FPS value reaches 37.3 frames/s,with an accuracy rate of 94.02％.
万方数据
维普
