计算机工程 ›› 2020, Vol. 46 ›› Issue (11): 279-285.doi: 10.19678/j.issn.1000-3428.0056061
徐国整1a, 廖晨聪1a, 陈锦剑1a, 董斌2, 周越1b
收稿日期:
2019-09-18
修回日期:
2019-10-28
发布日期:
2019-11-12
作者简介:
徐国整(1995-),男,硕士研究生,主研方向为智能检测;廖晨聪,助理研究员、博士;陈锦剑,教授、博士;董斌、周越,硕士研究生。
基金项目:
XU Guozheng1a, LIAO Chencong1a, CHEN Jinjian1a, DONG Bin2, ZHOU Yue1b
Received:
2019-09-18
Revised:
2019-10-28
Published:
2019-11-12
摘要: 针对混凝土结构表观裂缝检测准确率低、细节信息丢失及精度不高等问题,提出一种利用HU-ResNet卷积神经网络的混凝土表观裂缝检测方法。基于改进U-Net网络建立HU-ResNet模型,采用经ImageNet预训练的ResNet34残差网络作为编码器,以保留裂缝细节信息并加速网络收敛,引入scSE注意力机制模块在空间和通道重新标定编码块与解码块的输出特征,并利用超柱模块融合解码器各阶段所输出特征图获取更精确的裂缝图像语义信息和定位,同时采用组合损失函数进一步提高裂缝图像精度。实验结果表明,该模型的像素准确率、交并比和F1值分别达到0.990 4、0.693 3和0.816 6,优于Canny、区域生长等传统数字图像模型和FCN8s、U-Net、U-ResNet等深度学习模型且裂缝检测更精准。
中图分类号:
徐国整, 廖晨聪, 陈锦剑, 董斌, 周越. 基于HU-ResNet的混凝土表观裂缝信息提取[J]. 计算机工程, 2020, 46(11): 279-285.
XU Guozheng, LIAO Chencong, CHEN Jinjian, DONG Bin, ZHOU Yue. Extraction of Apparent Crack Information of Concrete Based on HU-ResNet[J]. Computer Engineering, 2020, 46(11): 279-285.
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