基于注意力机制和深层特征优化的混凝土路面裂缝检测  

Crack Detection of Concrete Pavement Based on Attention Mechanism and Deep Feature Optimization

作　　者：夏淑芳[1] 袁彬 瞿中[1] XIA Shufang;YUAN Bin;QU Zhong(School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学软件工程学院,重庆400065

出　　处：《计算机科学》2024年第11期198-204,共7页Computer Science

基　　金：国家自然科学基金(62176034)。

摘　　要：自动化裂缝检测是确保混凝土路面品质并提升道路养护效率的关键。针对现有方法在关注裂缝特征方面的不足以及深层特征图中裂缝细节信息易丢失的问题,文中提出一种融合注意力机制与深层特征优化策略的网络模型。该模型以VGG-16作为主干网络,首先,在主干网络的中高层卷积后引入一种轻量级的置换注意力机制,旨在提高网络对裂缝特征的敏感性;其次,为了进一步增强对裂缝特征的捕捉能力,在每个阶段的侧边输出中嵌入相应的注意力模块;最后,提出一种空间可分离金字塔模块并设计了一种注意力融合模块,用以优化深层特征图,还原更多的裂缝细节。侧网络通过在多个层次上融合低层和高层特征,辅助生成最终的预测图像。该网络采用二分类交叉熵损失函数作为评价函数,经过训练的网络模型能够在复杂背景下准确地从输入的原始图像中识别裂缝位置。为验证所提方法的有效性,在DeepCrack,CFD和Crack500这3个公开数据集上将其与6种方法进行了比较,所提算法表现出卓越的性能,F-score值达到了87.19%。Automatic crack detection is the key to ensure the quality of concrete pavement and improve the efficiency of road maintenance.Aiming at the shortcomings of existing methods in paying attention to crack features and the problem of easy loss of crack detail information in deep feature maps,this paper proposes a network model that integrates attention mechanism and deep feature optimization strategy,using VGG-16 as the backbone network.Firstly,a lightweight shuffle attention mechanism is introduced after the middle and high level convolutions of the backbone network,aiming to improve the sensitivity of the network to crack features.Secondly,in order to further enhance the capture ability of crack features,the corresponding attention module is embedded in the side output of each stage.Finally,a spatial separable pyramid module is proposed and an attention fusion module is designed to optimize the deep feature map and restore more crack details.The side network assisted in generating the final prediction image by fusing the low-level and high-level features at multiple levels.The network uses the binary cross-entropy loss function as the evaluation function,and the trained network model can accurately identify the crack position from the input original image under complex background.To verify the effectiveness of the proposed method,it is compared with six methods on three datasets,DeepCrack,CFD,and Crack500.The proposed algorithm shows excellent performance,and the F-score value reaches 87.19%.

关 键 词：裂缝检测 注意力机制 深层特征优化 多特征融合 置换注意力 空间可分离金字塔 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]