Ingot oxide slag detection using two-stage UNet network based on mixed supervised learning.

This paper presents a new method for detecting the amount of oxide slag on metal ingots, which is a critical indicator of the quality of the ingots. The challenge arises due to the irregular shape and small number of samples of the oxide slag, which makes it difficult to create a pixel-level label dataset for training a semantic segmentation network. To overcome this, a two-stage UNet network based on mixed supervised learning is proposed in this paper. The network uses a combination of a small number of pixel-level labels and a large number of weak label samples to reduce the cost of creating the dataset while achieving better detection accuracy. To solve the issue of overfitting due to the small number of samples, an improved generative adversarial network (GAN) is used to generate oxide slag images with arbitrary shapes to expand the dataset and improve the detection accuracy. Experiments show that the proposed network is highly effective in accurately detecting the distribution of oxide slag on metal ingots. Compared to existing segmentation algorithms, the network achieves higher accuracy while requiring a lower cost for dataset creation. The results of this work have important implications for the practical application of intelligent sensing in slag-picking robots. [ABSTRACT FROM AUTHOR]