基于机器视觉的预制节段梁锈迹检测  

Rust Detection of Prefabricated Segmental Beams Based on Machine Vision

作　　者：王成豪 王煜 高庭辉 赵成立 赵章焰[1] WANG Cheng-hao;WANG Yu;GAO Ting-hui;ZHAO Cheng-li;ZHAO Zhang-yan(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)

机构地区：[1]武汉理工大学交通与物流工程学院,武汉430063

出　　处：《科学技术与工程》2024年第26期11432-11440,共9页Science Technology and Engineering

基　　金：湖北省揭榜挂帅科技攻关项目(2021ZJKJJBGS01)。

摘　　要：传统的预制节段梁上的锈迹缺陷检测是依靠人工来进行识别,存在安全风险较高、检测的效率低、准确率不稳定等问题。为解决这些问题,提出了一种基于改进YOLOv5s的预制节段梁锈迹检测算法,在骨干网络模块中添加通道注意力和空间注意力融合的注意力机制(convolutional block attention module,CBAM),增强对特征的提取能力;在颈部网络模块中融合了双向加权特征金字塔网络结构(bi-directional feature pyramid network,BiFPN),提高网络的检测能力;用有效交并比损失函数EIoU与可以对损失函数进行幂次运算的Alpha-IoU进行结合,产生Alpha-EIoU损失函数替换完全交并比损失函数CIoU,降低损失值并进一步提高模型整体性能。试验结果表明,改进后的算法相较于原YOLOv5s算法,在准确率、召回率、mAP@0.5和mAP@0.5∶0.95等指标上分别提升了2.8%、3.0%、2.0%和5.4%,且没有增加过多的参数。经过三维建模软件3dsMax设计的虚拟场景的验证,该算法能在各种背景中达到理想的识别精度,有较强的鲁棒性,对于实现在预制节段梁锈迹检测上的部署有重要的理论意义和工程价值。The traditional detection of rust defects on segmental beams relies on manual identification,which poses high safety risks,low detection efficiency,and unstable accuracy.In order to solve these problems,a segment beam rust detection algorithm based on improved YOLOv5s was proposed,and the attention mechanism CBAM(convolutional block attention module)of channel attention and spatial attention fusion was added to the backbone network module to enhance the ability to extract features.The bi-directional weighted feature pyramid network structure BiFPN(bi-directional feature pyramid networ) was integrated into the neck network module to improve the network's detection ability.The efficient intersection over union loss EIoU was combined with Alpha-IoU,which can perform power operation on the loss function,to generate an Alpha-EIoU Loss function to replace the original complete intersection over union loss CIoU,reduce the loss value and further improve the overall performance of the model.After experiments,it has been shown that the improved algorithm outperforms the original YOLOv5s algorithm in terms of accuracy,recall mAP@0.5 and mAP@0.5∶0.95 have increased by 2.8%,3.0%,2.0%,and 5.4% respectively,without adding too many parameters.After the validation of a virtual scene designed by the 3D modeling software 3dsMax,this algorithm can achieve high recognition accuracy in various backgrounds and has strong robustness.It has important theoretical significance and engineering value for the deployment of rust detection in prefabricated segmental beams.

关 键 词：预制节段梁 锈迹检测 YOLOv5s 损失函数 3dsMax 

分 类 号：U446.3[建筑科学—桥梁与隧道工程]