吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 749-760.doi: 10.13229/j.cnki.jdxbgxb.20230945

• 交通运输工程·土木工程 • 上一篇    

基于改进YOLOv5s的桥梁螺栓缺陷识别方法

张洪1,2(),朱志伟2,胡天宇1,龚燕峰3,周建庭1()   

  1. 1.重庆交通大学 省部共建山区桥梁及隧道工程国家重点实验室，重庆 400074
    2.重庆交通大学 信息科学与工程学院，重庆 400074
    3.重庆交通大学 航运与船舶工程学院，重庆 400074
  • 收稿日期:2023-09-06 出版日期:2024-03-01 发布日期:2024-04-18
  • 通讯作者: 周建庭 E-mail:hongzhang@cqjtu.edu.cn;jtzhou@cqjtu.edu.cn
  • 作者简介:张洪（1987-），男，教授，博士.研究方向：桥梁健康监测及无损检测.E-mail：hongzhang@cqjtu.edu.cn
  • 基金资助:
    国家自然科学基金项目(52278291);重庆市自然科学基金项目(CSTB2022NSCQ-LZX0006);重庆交通大学研究生科研创新项目(2023S0083)

Bridge bolt defect identification method based on improved YOLOv5s

Hong ZHANG1,2(),Zhi-wei ZHU2,Tian-yu HU1,Yan-feng GONG3,Jian-ting ZHOU1()   

  1. 1.State Key Laboratory of Mountain Bridge and Tunnel Engineering，Chongqing Jiaotong University，Chongqing 400074，China
    2.School of Information Science and Engineering，Chongqing Jiaotong University，Chongqing 400074，China
    3.School of Shipping and Naval Architecture，Chongqing Jiaotong University，Chongqing 400074，China
  • Received:2023-09-06 Online:2024-03-01 Published:2024-04-18
  • Contact: Jian-ting ZHOU E-mail:hongzhang@cqjtu.edu.cn;jtzhou@cqjtu.edu.cn

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摘要：

针对现有算法在检测桥梁螺栓缺陷时因螺栓背景复杂和尺寸较小而导致的特征提取不充分、目标定位不精确问题，提出了一种基于改进YOLOv5s的桥梁螺栓缺陷识别方法。该方法在骨干网络中引入注意力机制以提升模型对螺栓特征的提取能力并加深对螺栓全局特征的关注度；优化空间金字塔池化结构以减少螺栓特征信息流失；采用MPDIoU作为边界框回归损失函数，提高螺栓边界框的回归精度；将YOLO检测头解耦以消除目标检测中分类任务和回归任务共享检测头对边界框位置回归的负面影响。在螺栓锈蚀、螺栓松动、螺栓脱落和螺母脱落4类典型缺陷螺栓以及正常螺栓的3810张自制螺栓图像数据集上进行训练和测试，实验结果表明：本文算法对螺栓缺陷的检测精度达到90.8%，相较于YOLOv5s提升了3%，均值平均精度达到92.6%，相较于YOLOv5s提升了4.3%，可以应用于桥梁螺栓的缺陷智能识别。

关键词: 桥梁工程, 螺栓缺陷识别, YOLOv5s, 桥梁螺栓

Abstract:

To address the issue of insufficient feature extraction and imprecise target localization in existing algorithms for detecting bridge bolt defects due to the complexity of bolt backgrounds and their small size， a bridge bolt defect recognition method based on enhanced YOLOv5s was proposed. Attention mechanisms in the backbone network was introduced to enhance the model's ability to extract bolt features and deepen its focus on global bolt characteristics. The spatial pyramid pooling structure was optimized to reduce the loss of bolt feature information. MPDIoU was employed as the bounding box regression loss function to improve the accuracy of bolt bounding box regression. The YOLO detection head was decoupled to eliminate the adverse effects of shared detection head in target detection on the regression of bounding box positions. Training and testing were conducted on 3810 self-made bolt image datasets of four typical defects： bolt rusting， bolt loosening， bolt detachment， and nut detachment， as well as normal bolts. Experimental results show that the algorithm achieves a detection accuracy of 90.8% for bolt defects， which is a 3% improvement over YOLOv5s， and a mean average precision of 92.6%， representing a 4.3% improvement over YOLOv5s. This method can be applied for intelligent recognition of bolt defects in bridges.

Key words: bridge engineering, bolt defect identification, YOLOv5s, bridge bolts

中图分类号: 

  • U448.14

图1

ECA模块"

图2

GAM模块"

图3

C3EGA模块"

图4

SPPFCSPC结构"

图5

MPDIoU计算方式"

图6

解耦头"

图7

螺栓缺陷识别模型"

图8

图像超分辨率修复"

图9

数据增强"

图10

数据集中样本框属性"

图11

数据集中缺陷螺栓数量"

表1

消融实验结果"

模型C3EGASPPFCSPCMPDIoU解耦头P/%R/%mF1/%mAP@0.5/%
YOLOv5s----87.881.084.388.3
A√---88.484.386.390.3
B-√--89.883.486.590.2
C--√-88.382.685.490.6
D---√87.684.386.090.2
E√√--89.286.187.691.2
F√√√-89.684.887.192.0
G√√√√90.886.388.592.6

图12

改进模型前、后热力图"

图13

改进模型前、后特征图"

表2

不同检测算法实验结果"

模型mAP@0.5/%模型大小/M
Faster-RCNN69.81024
SSD72.392.6
Centernet78.677.1
DETR81.4474
YOLOv5s88.314.4
YOLOv7-Tiny87.112.3
本文方法92.634.6

图14

不同检测算法效果"

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