为准确快速地识别出薄带铸轧钢板表面缺陷，提出了一种基于语义分割的薄带铸轧钢板表面缺陷分割方法。首先，通过Labeling软件对来源于薄带铸轧产线的钢板表面缺陷图像进行标注，创建缺陷分割数据集；其次，建立TransUNet网络模型对钢板表面缺陷进行识别和分割，引入优化的（dual attention network, DANet）双重注意力融合网络提升模型分割性能；最后，设计改进模型与其他分割模型的对比试验，根据试验结果和评价指标对改进模型进行分析，验证了本文方法的可行性和有效性。试验表明，改进网络针对薄带铸轧钢板表面异物缺陷的分割准确率为96.85%、平均交并比为96.99%、相似性系数达到92.98%，相较于TransUNet网络分别提升了1.19、0.61、0.63个百分点。此外，改进网络在公开的热轧带钢缺陷数据集上的分割准确率为92.86%，具有一定的通用性，可为钢板表面缺陷智能化检测提供技术指导。

A semantic segmentation-based method for defect segmentation on thin strip cast and rolled steel plates was proposed to accurately and quickly identify surface defects. Firstly, defect images from the production line were annotated using Labeling software to create a defect segmentation dataset. Secondly, a TransUNet network model was established to recognize and segment surface defects, integrating an optimized DANet dual-attention fusion network to enhance model segmentation performance. Finally, comparative experiments between the improved model and other segmentation models were designed. The feasibility and effectiveness of the proposed method are verified through analysis of experimental results and evaluation metrics. The experiments demonstrate that the improved network achieves a segmentation accuracy of 96.85%, an average intersection over union of 96.99%, and a similarity coefficient of 92.98% for foreign object defects on thin strip cast and rolled steel plates, respectively increasing by 1.19%, 0.61%, and 0.63% compared to the TransUNet network. Additionally, the improved network achieves a segmentation accuracy of 92.86% on the publicly available hot-rolled strip steel defect dataset, indicating its versatility and providing technical guidance for intelligent detection of surface defects on steel plates.