Ghost-YOLO:复杂环境下混凝土结构裂缝病害检测网络  

GHOST-YOLO:CRACK DISEASE DETECTION NETWORK OF CONCRETE STRUCTURE IN COMPLEX ENVIRONMENTS

作　　者：陈智丽[1] 张伍彪 王冰 李宇鹏 Chen Zhili;Zhang Wubiao;Wang Bing;Li Yupeng(School of Information and Control Engineering,Shenyang Jianzhu University,Shenyang 110168,Liaoning,China)

机构地区：[1]沈阳建筑大学信息与控制工程学院,辽宁沈阳110168

出　　处：《计算机应用与软件》2025年第2期171-180,共10页Computer Applications and Software

基　　金：国家自然科学基金项目(61602322);辽宁省重点研发计划项目(2019JH2/10100014);辽宁省教育厅重点攻关项目(lnzd201904)。

摘　　要：裂缝是混凝土结构桥梁最严重的病害之一,影响到整个桥梁结构的安全。提出一种新的Ghost-YOLO网络,用于检测不同环境下的混凝土结构裂缝病害。该网络有效结合GhostNet与YOLOv4网络优点,可在大幅减少网络模型参数的同时提高检测精度。为全面评估网络检测性能,构建不同环境下的大规模混凝土结构病害数据集,并应用迁移学习手段,成功将水上裂缝检测模型迁移至水下环境和户外实际工程环境。通过消融实验发现,Ghost-YOLO网络在不同复杂环境下均表现出较强的检测能力。将Ghost-YOLO网络与YOLOv4、Faster R-CNN、VFNet、YOLOF等先进的目标检测网络进行对比,结果显示Ghost-YOLO网络在裂缝检测准确度和速度方面都具有明显的优势。Cracks are one of the most serious diseases of concrete bridges,which affect the safety of the whole bridge structure.In this paper,a novel Ghost-YOLO network is proposed to detect cracks of concrete structures in different environments.This network effectively combined the advantages of GhostNet and YOLOv4 networks,which could greatly reduce the network model parameters and improve the detection accuracy.In order to comprehensively evaluate the detection performance of the network,this paper constructed a large-scale dataset of concrete structure diseases in different environments,and successfully transferred the above water crack detection model to the underwater environment and outdoor practical engineering environment by using the transfer learning method.Through ablation experiments,it is found that the Ghost-YOLO network shows strong detection ability in different complex environments.Compared with state-of-the-art object detection networks,such as YOLOv4,Faster R-CNN,VFNet,YOLOF,etc.,the results show that the Ghost-YOLO network has obvious advantages in accuracy and speed of crack detection.

关 键 词：深度学习 GhostNet YOLOv4 裂缝检测 水下 迁移学习 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]