基于注意力解码和连续性监督的裂缝检测方法  

Crack Detection Method Based on Attention Decoder and Continuous Supervision

作　　者：谢永华[1] 卓安南 XIE Yonghua;ZHUO Annan(School of Computer Science,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学计算机学院,南京210044

出　　处：《计算机工程与应用》2025年第4期122-129,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(62076123)。

摘　　要：裂缝检测是预防重大建筑坍塌事故的重要措施。针对裂缝特征微弱和图像背景噪声干扰较多的问题,引入语义分割领域的编码解码结构算法来提升检测鲁棒性。为了解决编码解码结构网络中解码方式单一且不能高效联系编码特征语义信息的缺陷,设计了基于注意力机制的解码模块,通过注意力机制增强解码器对裂缝特征的关注度,优化了模型的解码效果;对于模型预测结果中裂缝断裂问题,改进了连续性监督算法,通过在模型预测阶段加入连续性邻接图预测,并结合对应的损失函数进行监督,提高预测结果中裂缝的特征连续性;最后结合这两种方法搭建了一种编码解码结构的自动化裂缝检测模型。通过在两个数据集上的实验结果,验证了所提方法的优越性,在选取的常用算法中取得了最高的检测精度。Crack detection is an important measure to prevent building collapse accidents.Aiming at the weak crack feature and interference from noise in crack images,encoder-decoder nets in semantic segmentation are introduced to improve the detection robustness.An attention decoder module which enhances the attention of the crack feature in decoder through attention mechanism is designed to address the shortcomings of the simple decoder in traditional encoder-decoder nets and the low efficiency of connecting the semantic information in encoding features.For the problem of cracks fracture in predictions,the continuous supervision algorithm is improved,by adding adjacency connected predictions and combining the corresponding loss function for supervision,the continuity of crack features in prediction results is improved.Combining these two methods,an automatic encoder-decoder crack detection model is proposed.The experimental results on two datasets verify the superiority of the proposed model,the best detection accuracy is achieved among the commonly used models selected.

关 键 词：裂缝检测 语义分割 编码解码 注意力机制 连续性监督 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] U457[自动化与计算机技术—计算机科学与技术]