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作 者:刘晏长 LIU Yanchang(Beijing Yiyuan Landscape Engineering Co.,Ltd.,Beijing 100076,China)
机构地区:[1]北京艺苑风景园林工程有限公司,北京100076
出 处:《无损检测》2025年第4期33-38,共6页Nondestructive Testing
摘 要:为提升装配式钢结构建筑的安全性与可靠性,设计了一种装配式钢结构建筑抗侧力支架缺陷超像素级Gabor识别方法。首先对抗侧力支架缺陷图像实施归一化处理,通过图像旋转、错切变换、镜像翻转处理扩充数据集。然后利用由多模态特征抽取机制、鲁棒像素相似度评估以及像素至超像素软映射策略组成的基于多任务学习的图像超像素分割方法实施抗侧力支架缺陷图像超像素分割。最后通过二维Gabor滤波器对生成的超像素实施Gabor滤波,提取超像素特征,依据提取的Gabor局部相位特征与Gabor局部方向特征,通过支持向量回归(SVR)实现抗侧力支架缺陷识别。试验结果表明,设计方法能够识别各种抗侧力支架缺陷,对于所有尺寸的缺陷,设计方法的假阳性率均较低,对于精细缺陷的识别假阳性率仅为0.025。In order to improve the safety and reliability of prefabricated steel structure buildings,a superpixel level Gabor identification method for the defects of anti-lateral force brackets in prefabricated steel structure buildings was designed.The images of defects in the anti-lateral force bracket were normalized.The dataset was expanded through image rotation,cropping transformation,and mirror flipping processing.A multi-task learning based image superpixel segmentation method consisting of multimodal feature extraction mechanism,robust pixel similarity evaluation,and pixel to superpixel soft mapping strategy was used to implement anti lateral force stent defect image superpixel segmentation.For generating superpixels,their Gabor filtering was implemented through a two-dimensional Gabor filter to extract superpixel features.Based on the extracted Gabor local phase features and Gabor local directional features,SVR was used to identify defects in anti-lateral force brackets.The experimental test results showed that the design method could identify various lateral force resistant bracket defects.For all sizes of defects,the false positive rate of the design method was low,and the false positive rate for identifying fine defects was only 0.025.
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