| 周淑伊,刘小燕,陈玉如,孙浠汉.基于可调证明滤波器的球团矿生球图像裂缝检测方法[J].电子测量与仪器学报,2022,36(7):125-135 |
| 基于可调证明滤波器的球团矿生球图像裂缝检测方法 |
| Crack detection for iron ore green pellet imagebased on steerable evidence filter |
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| DOI： |
| 中文关键词: 裂缝检测 裂缝分割 可调证明滤波器 球团矿生球 |
| 英文关键词:crack detection crack segmentation steerable evidence filter iron ore green pellet |
| 基金项目:国家自然科学基金（61973108）、湖南省科技计划（2020GK2025）项目资助 |
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| 中文摘要: |
| 球团矿生球制造是炼钢工业中的重要环节,而裂缝检测是生球质量指标(落下强度)检测中的关键步骤。 目前生球裂
缝检测仍采用人工目测,效率低且检测结果具有主观性,而现有的基于图像处理的裂缝自动检测方法主要针对桥梁、道路、太阳
能电池等物体的平直表面上的裂缝,对生球这种小目标曲面上的裂缝检测能力有限,且难以克服生球图像中原材料污渍、生球
边缘轮廓、球面反光等多种噪声的干扰,易造成裂缝的误检与漏检。 针对这一问题,提出一种基于可调证明滤波器( SEF)的球
团矿生球图像裂缝检测方法。 首先,针对图像背景中存在的原材料污渍与碎屑干扰,采用主动轮廓模型从图像中分割出感兴趣
区域(AOI);然后,针对生球边缘轮廓和球面反光造成的干扰,采用可调证明滤波器生成生球裂缝响应图,实现 AOI 图像粗分
割;接着,利用形态学处理与连通域分析法消除粗分割图像中的生球边缘及噪声点,获得精确的分割结果;最后,采用连通域分
析法检测生球是否存在裂缝,并计算出裂缝条数。 为验证所提出的方法,搭建了生球图像实验采集平台,采集了约 300 张具有
不同背景和裂缝数量的生球图像。 实验结果表明,与现有的 5 种裂缝检测方法相比,本文方法在裂缝分割准确率、精确率、加权
调和平均评价指标上均具有明显优势,检测生球是否存在裂缝的准确率为 96%,检测裂缝数量的准确率为 90%。 本文研究成
果为生球落下强度质量指标的自动化、智能化检测奠定了基础。 |
| 英文摘要: |
| Manufacturing of iron ore green pellet is a significant step in metallurgy industry. Crack detection for green pellet is a key step
in the measuring process of the important pellet quality metric (drop strength). However, current image-based methods are mainly used
to detect cracks on the flat surface of bridges, roads and solar cells. Therefore, their crack detection ability is limited on pellet with
curved surface, and is easily affected by the raw material stains, pellet edge contour, strong light reflection or other interferences in
pellet images, resulting in the false detection or missed detention. To solve the problem, a crack detection method for green pellet based
on steerable evidence filter (SEF) is proposed. Firstly, the target area of green pellet is segmented by the active contour model, which
is used to eliminate the raw material stains in the image background. In order to overcome the interfaces of pellet edge contour and strong
light reflection, steerable evidence filter is used to generate the response map of pellet crack, followed by the morphological processing
and connected domain analysis method used to eliminate the pellet edge response and noise in response map of pellet crack, so that more
accurate crack segmentation results can be obtained. Finally, the connectivity domain method is used to detect cracks and calculate the
number of cracks. In order to verify the proposed method, experimental platform was built to establish dataset of green pellet, and about
300 green pellet images with different backgrounds and the number of cracks were captured. Results show that our method outperforms
five crack detection methods in crack segmentation metrics including accuracy, precious, F1. Accuracy of detecting cracks in pellet is
96%, and the accuracy of detecting the number of cracks is 90%. The crack detection results lay a foundation for the automatic and
intelligent detection of drop strength quality metric of green pellet. |
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