Prediction of penetration based on infrared thermal and visual images during pulsed GTAW process

Abstract

Abstract Aiming at improving the recognition accuracy and robustness of the penetration state recognition model, a Dual-input Faster R-CNN (region-convolutional neural network) model with the input of original infrared thermal (IR) and visual (CCD) image was established. For avoiding the negative effects on recognition caused by arc flicker inside CCD image and irrelevant information inside background, synchronous feature extraction, convolutional descriptor selection and recognition based on synthetic features were designed in model. Since the industrial personal computer running the model is usually not equipped with powerful GPU and enough memory space, and sometimes it needs to train a specific model according to the new data set, the model is required to have high recognition accuracy, low recognition time, short training time and small occupation space. By sharing RPN (region proposal network) and ROI (region of interest) Pooling Layer inside Faster R-CNN and introducing Label-integrated Layer, the above requirements can be greatly met. The recognition accuracy of convolutional descriptor selection assisted Dual-input Faster RCNN reached more than 95%, while the recognition time for each IR&CCD-image data pair was less than 270\xa0ms.