A Deep Meta-Metric Learning Method for Few-Shot Weld Seam Visual Detection

Deep learning-based object detection algorithms are gradually promoted in industrial visual detection due to their versatility and high accuracy. These algorithms usually require large amounts of training data, however there is a problem of lack of training samples in actual weld seam detection tasks that challenges the weld seam visual detection task. To improve the performance on weld seam detection, especially for those few-shot tasks, this paper proposes a meta-metric learning method for few-shot weld seam detection. The method introduces a distance metric-learning module besides the meta-learning algorithm. By optimizing the training strategy and classification mode of the base detection model, the method accelerates the training process and improves the learning capability on few-shot weld seam samples. Compared with the base model, the mAP of the method proposed in this paper on the weld seam dataset is improved by about 8.9%.