摘要
针对当前混凝土建筑裂缝走向不规则、细小裂缝特征难以提取的问题,提出一种基于YOLOv4改进的混凝土建筑裂缝检测算法.该算法以YOLOv4框架为基础,在其特征提取网络部分引入感受野更宽的RFB模块捕获特征图;并基于PANet多尺度路径融合结构,提出新的多尺度特征融合方式SL-PANet.该方式首先增加浅层网络特征信息,提高模型对细小裂缝识别的精度,其次采用DUpsampling上采样模块充分还原图像的特征信息,并在上采样和下采样过程中融入CBAM注意力机制模块,突出裂缝的特征信息,去除背景冗余信息的干扰,以此增强裂缝特征的表达能力.该算法同时利用AdamW优化器加快网络训练的收敛.实验结果表明:文章改进的算法检测精度高达94.47%,较原YOLOv4算法提高6.44%,能够满足当前混凝土建筑裂缝检测需求.
To solve the problem of irregular crack trend and difficult to extract the characteristics of small cracks in concrete buildings,an improved crack detection algorithm based on YOLOv4 was proposed.Based on YOLOv4 framework,RFB module with wider receptive field is introduced in the feature extraction network to capture feature images.Based on the multi-scale path fusion structure of PANet,a new multi-scale feature fusion method sl-PANET is proposed.Firstly,the shallow network feature information is added to improve the accuracy of the model in identifying fine cracks.Secondly,the upper sampling module of DUpsampling is adopted to fully restore the image feature information.The CBAM attentional mechanism module was incorporated in the up-sampling and down-sampling processes to highlight the fracture feature information and remove the interference of background redundant information,so as to enhance the expression ability of fracture feature.The AdamW optimizer is also used to accelerate the convergence of network training.Experimental results show that the detection accuracy of the improved algorithm is as high as 94.47%,which is 6.44%higher than the original YOLOv4 algorithm,and can meet the current crack detection requirements of concrete buildings.
作者
石颉
马文琪
吴宏杰
SHI Jie;MA Wenqi;WU Hongjie(Suzhou University of Science and Technology,Suzhou 215009,China;Jiangsu Provincial Key Laboratory of Building Intelligent Energy Conservation,Suzhou 215009,China)
出处
《微电子学与计算机》
2023年第3期56-66,共11页
Microelectronics & Computer
基金
国家自然科学基金项目(62073231)。
