基于改进Res-UNet网络的钢铁表面缺陷图像分割研究

In order to improve the efficiency and accuracy of steel quality images detection and promote the automation level of industry, an improved Res-UNet segmentation algorithm is proposed. ResNet50 is used instead of ResNet18 as the encode module to enhance feature extraction capability. Structure like DenseNet is added to encode module, which helps to make full use of shallow feature maps. A new loss function combining weighted Dice loss and weighted Binary Cross Entropy loss (BCEloss) is used to alleviate data imbalance. Data set enhancement strategy ensures that the network learns more features and improves the segmentation accuracy. Compared with the classic UNet, the Dice coefficient of the improved Res-UNet increases by 12.64% and reaches 0.7930. In all, the improved Res-UNet achieves much better accuracy on various defects while requires much less training efforts. The algorithm proposed by this paper is of practical use in the field of steel surface defect segmentation.