Real-time estimation model for magnetic arc blow angle based on auxiliary task learning

Magnetic arc blow during the welding of submersible pressure hulls can severely impact the weld quality. Real-time estimation of the arc posture by the teaching-replay welding robot is a crucial and challenging step for further correction of the magnetic blow. This paper proposes a lightweight deep learning model capable of real-time estimation of the welding arc posture. The model takes arc images as input and outputs the angle of arc magnetic blow end-to-end. Specifically, to enable the model to learn the prior knowledge of human perception of arc magnetic blow angles, a training strategy utilizing keypoint detection as an auxiliary task has been proposed. This approach enhances the estimation accuracy of the model without incurring additional inference costs. Additionally, an efficient multi-scale attention (EMA) mechanism was integrated into the angle prediction branch to facilitate the learning of long-range feature dependencies. To confirm the effectiveness of the model, an arc magnetic blow image dataset was constructed for training and testing. The experimental results show that the model achieves a cumulative score (${\mathrm{CS}}_3$) of 98.47 % and a mean absolute error (MAE) of 0.9985° during testing. The model achieves an inference speed of 70.49 FPS on an Intel® Core™ i7-8750H CPU, which satisfies the criteria for real-time monitoring.