STUDY AND APPLICATION OF IMAGE WATER LEVEL RECOGNITION CALCULATION METHOD BASED ON MASK RCNN AND FASTER R-CNN.

To measure the water level with a water level gauge, an automatic water level calculation method based on Region-based Convolutional Neural Networks (R-CNN) is proposed in this study. First, the water level gauge is located and the contour of water level gauge is obtained using the enhanced Mask R-CNN model approach. Secondly, using the contour of the water gauge, image processing technology is utilized to recognize and intercept the image that only contains the water gauge. Next, the scale on the water level gauge is detected and identified using the Faster R-CNN (Region-based Convolutional Neural Networks) model approach. The projection transformation method then calculates the value of the current water level based on the scale of the identified water level. The proposed approach was demonstrated by experimental findings to be relatively straightforward, to have high recognition accuracy and ability in complex interference environments in the field, and to have low cost and great application value. The findings presented in this paper can be used to help with automatic water level gauge measuring both theoretically and practically. [ABSTRACT FROM AUTHOR]