基于深度学习的图像板形检测方法研究

doi:10.3901/JME.260185

摘要/Abstract

摘要： 智能制造是钢铁行业转型升级的关键方向，过程参数与产品质量的精准数字感知是基础与重点。板形是带材的重要质量指标之一，板形在线检测技术是生产高级精品带材的必然选择。目前国内板形仪配备率低，进口设备成本高昂，多数企业仍依赖人工判断，质量稳定性与智能化水平不足。为此，需要对板形检测机理进行创新研究，旨在保证检测精度的条件下，降低检测设备成本，提升检测技术的通用性。目前，新形式的机器视觉理论与技术(深度学习)取得了快速发展，采用机器视觉技术实现板形检测具有理论可行性，也是一种必然趋势。为研究基于机器视觉的冷轧带材板形检测方法，通过一系列理论和技术创新，建立了多层次的智能模型，实现了带材位置区域检测、图像板形(图像)及应变板形(应变)深度表征、表征因子定量映射，最终实现了图像板形至应变板形之间的精准映射。研究结果为推进板形检测技术智能化的落地提供了思路和具体方案，有助于行业实现转型升级。

关键词: 冷轧带材, 图像板形, 应变板形, 深度学习, 机器视觉, 智能检测

Abstract: Intelligent manufacturing is recognized as a key direction for the transformation and upgrading of China’s steel industry. The accurate digital perception of process parameters and product quality is regarded as its foundation and priority. Flatness is a critical parameter for cold-rolled strip products, and the development and application of online flatness detection technology is thus essential for the production of high-quality strip. However, the deployment rate of flatness meters in the domestic steel industry remains low, with imported equipment being prohibitively expensive. Consequently, most enterprises still rely on manual assessment, leading to inconsistent product quality and insufficient levels of intelligentization. There is an urgent need for innovative research into flatness detection mechanisms, with the goal of reducing equipment costs and enhancing the universality and adaptability of detection technologies without compromising detection accuracy. In recent years, the theory and application of machine vision, particularly deep learning, have developed rapidly. Applying machine vision technology to flatness detection is not only theoretically feasible but also an inevitable trend in the advancement of intelligent manufacturing. Through theoretical and technological innovations, a series of multi-level intelligent models were developed to accomplish strip position detection, deep representation of image and strain flatness, quantitative mapping of representation factors, and finally, a precise image-to-strain flatness mapping. The proposed models and framework provide insights and a concrete scheme for intelligent flatness detection, paving the way for the industry’s technological transformation.

Key words: cold rolled strip, image flatness, strain flatness, deep learning, machine vision, intelligent detection

中图分类号:

TG335

徐扬欢, 王东城, 刘宏民, 党丽莹, 刘云飞, 杨爱民. 基于深度学习的图像板形检测方法研究[J]. 机械工程学报, 2025, 62(6): 185-196.

XU Yanghuan, WANG Dongcheng, LIU Hongmin, DANG Liying, LIU Yunfei, YANG Aimin. Research on Image Flatness Detection Method Based on Deep Learning[J]. Journal of Mechanical Engineering, 2025, 62(6): 185-196.

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http://www.cjmenet.com.cn/CN/Y2025/V62/I6/185

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