摘要
缺陷检测是带钢生产过程中不可缺少的工序,现有检测方法普遍存在检测精度较低、实时性差等问题。为解决上述问题,本文提出了一种基于轻量化YOLOv3的快速缺陷检测方法。MobileNetv2作为主干网络并用两个尺度的特征图进行输出,保证了网络模型的轻量化;将改进后的注意力模块融合进特征金字塔网络(feature pyramid network,FPN),同时结合空间金字塔池化模块(spatial pyramid pooling,SPP),以提高算法对缺陷的学习能力;使用K均值聚类算法获得更优的先验框,并且使用CIoU(complete-intersection over union)对损失函数进行优化,进一步提升网络性能。提出的方法在带钢缺陷数据集上检测速度为70.8 FPS;模型参数量为7.1 MB,仅为YOLOv3的3.02%。实验结果表明本文所提方法能够在保证精度的同时实现对缺陷的快速检测,具有良好的生产线部署能力。
Defect detection is an indispensable process in the strip steel production process,and existing inspection methods generally have problems such as low detection accuracy and poor real-time performance.To solve the above problems,a fast defect detection method based on lightweight YOLOv3 is proposed in this paper.MobileNetv2 is used as the backbone network and output with two scales of feature maps,so that the lightweight of the network model is guaranteed;the improved attention module is fused into the feature pyramid network(FPN)and the network is combined with the spatial pyramid pooling(SPP)to improve the learning ability of the algorithm for defects;the K-means mean clustering algorithm is used to obtain a better anchor box,and the complete-intersection over union(CIoU)is used to optimize the loss function to further improve the network performance.The proposed method has a detection speed of 70.8 FPS on the strip steel defect dataset;the number of model parameters is 7.1 MB,which is only 3.02%of YOLOv3.Experiments show that the proposed method can achieve rapid detection of defects while ensuring accuracy,and has good production line deployment capabilities.
作者
马千文
刘国华
MA Qianwen;LIU Guohua(School of Mechanical Engineering,Tiangong University,Tianjin 300387,China;Advanced Mechatronics Equipment Technology Tianjin Major Laboratory,Tianjin 300387,China)
出处
《光电子．激光》
CAS
CSCD
北大核心
2023年第7期743-751,共9页
Journal of Optoelectronics·Laser
基金
天津市科技计划项目(21YFFCYS00080)资助项目。
