Adaptive reading recognition algorithm of pointer instrument based on adversary learning
To address the problems of high cost,inaccuracy,and low efficiency in manual reading of pointer instruments,a pose-invariant adaptive reading recognition algorithm of pointer instrument based on adversarial learning is proposed.This algorithm utilizes an object detection model to identify the position and attitude of the pointer instrument in the image,calibrates the pointer instrument and uses digital image processing technology for reading recognition.In order to improve the recognition effectiveness of the object detection model on pointer instrument images with different poses,a data augmentation technology based on adversarial learning is proposed,which constructs training data by optimizing rotation angles,translation distances,and scaled ratios of images that lead to inaccurate recognition,to improve the accuracy of the object detection model when the pointer instrument's pose changes.The research focuses on the SF6 pressure instrument commonly used inindustrial and mining enterprises,and experimental results show that the error of reading recognition is within 1%,which proves the effectiveness of the proposed algorithm.
万方数据
维普
