• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2026, Vol. 62 ›› Issue (10): 146-158.doi: 10.3901/JME.260339

• 材料科学与工程 • 上一篇    

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考虑振动影响的冷轧板带表面缺陷多模态分割方法研究

杜旺哲1,2,3, 王涛1, 张飞宇1, 刘亚星1,2,3,4, 牛小淼1,2,3, 刘元铭1,2,3, 王涛1,2,3, 黄庆学1,2,3   

  1. 1. 太原理工大学机械工程学院 太原 030024;
    2. 太原理工大学先进金属复合材料成形技术与装备教育部工程研究中心 太原 030024;
    3. 金属成形技术与重型装备国家重点实验室 西安 710018;
    4. 海安太原理工大学先进制造与智能装备产业研究院 海安 226600
  • 收稿日期:2025-05-09 修回日期:2025-12-07 发布日期:2026-07-29
  • 作者简介:杜旺哲,男,1995年出生,博士,副教授。主要研究方向为缺陷检测、多模态信息融合、尺寸测量、深度学习和计算机视觉。E-mail:duwangzhe@tyut.edu.cn;刘亚星(通信作者),男,1990年出生,博士,副教授。主要研究方向为极薄带轧制工艺与表面质量控制。E-mail:liuyaxing@tyut.edu.cn
  • 基金资助:
    国家重点研发计划(2023YFB2307604)、国家自然科学基金(52305406)、山西省基础研究计划(202303021212046,202303021212054)、金属成形技术与重型装备全国重点实验室开放课题(S2308100.W15,S2308100.W17,S2308100.W21)和海安太原理工大学先进制造与智能装备产业研究院开放研发(2024HA-TYUTKFYF002)资助项目。

Research on Multi-modal Segmentation Method for Surface Defects of Cold-rolled Steel Strips Considering Vibration Effects

DU Wangzhe1,2,3, WANG Tao1, ZHANG Feiyu1, LIU Yaxing1,2,3,4, NIU Xiaomiao1,2,3, LIU Yuanming1,2,3, WANG Tao1,2,3, HUANG Qingxue1,2,3   

  1. 1. College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024;
    2. Engineering Research Center of Advanced Metal Composites Forming Technology and Equipment of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024;
    3. National Key Laboratory of Metal Forming Technology and Heavy Equipment, Xi'an 710018;
    4. Hai'an Taiyuan University of Technology Advanced Manufacturing and Intelligent Equipment Industry Research Institute, Hai'an 226600
  • Received:2025-05-09 Revised:2025-12-07 Published:2026-07-29

摘要: 针对当前冷轧板带表面高精度在线缺陷检测需求,设计并搭建一种冷轧板带表面多模态检测平台,实现板带表面二维RGB图像与三维点云的同步采集。针对在生产工况中由振动与板形干扰等因素造成三维点云失真等问题,提出一种考虑振动干扰的深度图转化方法,其由坐标投影变换、板形拟合滤除和深度栅格映射三个模块构成,可有效补偿振动噪声等并生成高保真深度图。提出一种冷轧板带表面缺陷多模态分割网络,采用双流编码-双支路解码架构,集成多尺度特征解析模块,引入“逐级输出监督+深浅层双重监督”的混合监督策略。试验结果表明,该网络在冷轧板带表面多模态缺陷数据集上实现最优的缺陷分割性能,推理速度达68 FPS,验证了其在振动干扰下对冷轧板带表面进行高精度在线缺陷检测的可行性。

关键词: 板带表面缺陷检测, 三维点云处理, 多模态分割, RGB-D网络, 深度学习

Abstract: To meet the current demand for high-precision online defect detection of cold-rolled strips, a cold-rolled strip surface multi-modal detection platform is designed and built, enabling synchronous acquisition of two-dimensional RGB images and three-dimensional point clouds of the strip surface. To address distortions in three-dimensional point clouds caused by vibration and strip-profile interference under production conditions, a depth map transformation method considering vibration interference is proposed and is composed of three modules: coordinate projection transformation, surface-fitting noise removal, and depth raster mapping; vibration noise is effectively compensated and high-fidelity depth maps are generated. A multi-modal segmentation network for cold-rolled strip surface defects is proposed, in which a dual-stream encoder–dual-branch decoder architecture is adopted, a multi-scale feature analysis module is integrated, and a hybrid supervision strategy of stage-wise output supervision plus dual deep–shallow supervision is introduced. Experimental results indicate that optimal defect segmentation performance is achieved on the cold-rolled strip surface multimodal defect dataset and an inference speed of 68 FPS is attained, thereby validating the feasibility of high-precision online defect detection of cold-rolled strip surfaces under vibration interference.

Key words: strip surface defect detection, 3D point cloud processing, multi-modal segmentation, RGB-D network, deep learning

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