基于YOLOv7的钢表面缺陷检测
Steel surface defect detection based on YOLOv7
张亚腾 1黄俊1
作者信息
- 1. 重庆邮电大学通信与信息工程学院,重庆 400065
- 折叠
摘要
针对钢表面缺陷检测中存在误检率和漏检率高问题,提出了一种基于YOLOv7改进的钢表面缺陷检测算法.在该算法中,通过引入ConvNeXt-CBS模块以增强网络的特征提取能力,同时,基于SimAM注意力机制构建了 MPCS模块,提高网络对微小缺陷目标的关注度,最后在模型中引入C3模块来提升网络稳定性.实验结果表明,在NEU-DET数据集上,该算法的检测精度达到80.2％,比YOLOv7算法高出3.9％.与以往的钢表面缺陷检测算法相比,该算法具有更高的检测精度和更快的检测速度,可在工业应用中发挥重要作用.
Abstract
A steel surface defect detection algorithm based on improved YOLOv7 is proposed to address the high false detection rate and missed detection rate in steel surface defect detection.In this algorithm,the ConvNeXt-CBS module is introduced to enhance the feature extraction capability of the network.Additionally,the MPCS module is constructed based on the SimAM attention mechanism to increase the network's focus on small defect targets.Finally,the C3 module is introduced in the model to improve network stability.Experimental results show that on the NEU-DET dataset,the detection accuracy of this algorithm reaches 80.2％,which is 3.9％higher than the YOLOv7 algo-rithm.Compared to previous steel surface defect detection algorithms,this algorithm achieves higher detection accura-cy and faster detection speed,making it highly suitable for industrial applications.
关键词
目标检测/缺陷检测/YOLOv7/ConvNeXt-CBS/SimAMKey words
object detection/defect detection/YOLOv7/ConvNeXt-CBS/SimAM引用本文复制引用
出版年
2024
NETL
NSTL
万方数据