基于深度学习的水下混凝土结构表观病害智慧识别方法-《建筑科学与工程学报》

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Title:: Intelligent identification method of apparent defects in underwater concrete structures based on deep learning

作者:: 陈德津，张东林，叶锡钧，郑文智，周军勇; 广州大学土木与交通工程学院，广东广州 510006

Author(s):: CHEN Dejin, ZHANG Donglin, YE Xijun, ZHENG Wenzhi, ZHOU Junyong; School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, Guangdong, China

关键词:: 水下混凝土; YOLOv9：图像融合; 两阶段识别

Keywords:: underwater concrete; YOLOv9; image fusion; twostage recognition

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文献标志码:: A

摘要:: 在水下环境中，受水介质的散射和吸收以及水流的影响，易导致水下图像模糊、雾化、失真及分辨率低，从而使基于深度学习的结构病害自动化识别准确率不高。通过两阶段的处理，搭建一个结合图像融合算法与目标识别算法为一体的模型：阶段1通过图像融合算法来增强病害的可检测性，该算法采用改进的CycleGAN与带颜色保护的多尺度Retinex算法(MSRCP)实现特征图像的权重融合，其中CycleGAN的改进包含三部分，分别为优化循环一致性损失、替换激活函数及更换注意力机制，能有效增强图像特征提取能力，促进了更高效的训练过程，并减轻了模型权重；阶段2通过YOLOv9目标识别算法，对复杂水域下水下结构表观病害类型进行快速、准确识别与分类。结果表明：通过对水下结构表观病害图像的增强，该方法的mAP0.5识别率达到74.3%，相较于未经处理的情况，识别率提升了11.6%；对“剥落”、“露筋”和“裂缝”3种经典表观病害的识别，该方法的识别准确率分别为74.5%、58.5%和90.0%，相较于未经处理的情况，分别提升了1.9%、5.6%和26.1%。

Abstract:: In underwater environment, the scattering and absorption of water media and the influence of water flow, it is easy to cause blurring, atomization, distortion and low resolution of underwater images, which subsequently reduces the accuracy of automated structural defect identification based on deep learning. Through twostage processing, a model integrating image fusion algorithm and target recognition algorithm was established. In stage 1, the image fusion algorithm was used to enhance the detectability of defects. The algorithm achieved feature image weight fusion using an improved CycleGAN and a multiscale retinex algorithm with color protection (MSRCP). The improvements to CycleGAN included three parts, which were optimizing the cycle consistency loss, replacing the activation function, and incorporating a new attention mechanism. These enhancements could effectively enhance the image feature extraction capability, facilitate a more efficient training process, and reduce model weights. Stage 2 employed the YOLOv9 object recognition algorithm to achieve rapid and accurate identification and classification of underwater structural surface defect types in complex water environment. The results show that by enhancing underwater structural surface defect images, the mAP0.5 identification rate of the proposed method reaches 74.3%, which is 11.6% higher than that of the untreated case. The recognition accuracy of the proposed method is 74.5 %, 58.5 % and 90.0 % for the recognition of three classic apparent diseases of “concrete spall”, “exposed rebar”, and “concrete cracking”, and compared with the untreated case, it is increased by 1.9 %, 5.6 % and 26.1 %, respectively.

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

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