Key Points Recognition and Deformation Correction Method for Pointer Instrument Based on Improved RT-DETR Model

Abstract The parameter identification accuracy will be affected greatly by the skew and rotating pointer instrument image captured in industrial environments, regarding this issue, a deformation correction method for pointer instrument based on improved RT-DETR model is proposed. Firstly, the inner endpoints of the long scale line and the rotation center point are taken as the key points for ellipse fitting by the least squares method. And then, based on the corresponding relationship between the rotation center and the fitted ellipse’s center, the elliptical and perspective transformation methods are used to correct the skew and rotating pointer instrument image for first-stage. Next, based on the symmetrical relationship between the zero and full scale marks of key points, the rotation transformation method is used to correct the skew and rotating pointer instrument image for second-stage. In order to improve the extraction accuracy and real-time performance for key points, the FCG module is constructed to optimize the backbone network of RT-DETR model based on the Faster Net network, and the CGSFR-FPN, a spatial feature reconstruction network based on context-guided, is introduced to enhance feature fusion of RT-DETR model. Ablation and comparative experiments show that the optimized model comparing with the original model, while maintaining the mAP@0.5-0.95 unchanged, reduces the number of parameters by 25.72%, the computational cost by 34.21%, the model weight by 25.68%, and achieves an FPS of 218.4 frames per second. Instrument reading experiments show that the average relative reading error and the average absolute error using the proposed method are 2.033% and 0.427% respectively, verifying the superiority, effectiveness, and generalizability of the proposed method.