Automatic recognition algorithm of digital instrument reading in offshore booster station based on Mask-RCNN

The offshore booster station adopts the rail hanging robot to carry out patrol inspection, and the machine vision method is used to automatically identify the digital instrument reading instead of manual recording. An automatic recognition algorithm of digital instrument reading based on Mask-RCNN deep learning method was presented. The original images of different types of digital instruments were made into data sets, trained by deep learning algorithm, the parameters of the algorithm were optimized according to the change curve of loss function, the trained model was obtained, and then the digital instrument images were recognized and analyzed. The gray world algorithm and Hough transform were used for image preprocessing, which can effectively improve the accuracy of digital recognition. Finally, the recognition performance of YOLOv3 and Mask-RCNN deep learning algorithm was compared in the experiment. The results show that the former has higher detection speed and the latter has higher accuracy. The recognition rate of the latter is 99.52%, it meets the requirement that remote monitoring of offshore booster station requires high accuracy of digital instrument reading.