基于改进YOLOv8的热轧带钢表面缺陷检测方法
Surface Defect Detection Method for Hot-rolled Strip Steel Based on Improved YOLOv8
肖科 1杨昕宇 1韩彦峰 1宋斌2
作者信息
- 1. 重庆大学 机械与运载工程学院,重庆 400030
- 2. 珞石(山东)机器人集团有限公司,山东 济宁 275312
- 折叠
摘要
针对目前热轧带钢表面缺陷检测精度低和效率低的问题,提出了一种基于改进YOLOv8s的目标检测算法.首先,提出了一种基于特征图二次拼接并融入GAM的SPPD模块,提升了模型多尺度信息融合能力.其次,提出了一种融合可变形卷积的特征提取模块DCN-block,以增大模型的感受野,提取完整的缺陷信息.最后,将特征融合网络中的C2f模块替换为BoT(bottleneck transformer)结构,将Transformer中的多头自注意力机制与卷积融合,提升模型的全局位置信息感知能力.实验结果表明,本文提出的算法在NEU-DET数据集上的平均精度均值(mAP)达到了80.5%,较原有的YOLOv8算法提升了5个百分点,同时检测速度达到了83帧/s,满足实时检测的需求.
Abstract
A object detection algorithm based on improved YOLOv8s is proposed to address the issues of low accuracy and low efficiency in surface defect detection of hot-rolled strip steel.Firstly,an SPPD module based on feature map secondary stitching and incorporating GAM is proposed,which enhances the model's multi-scale information fusion ability.Secondly,a feature extraction module DCN-block that integrates deformable convolution is proposed to increase the receptive field of the model and extract complete defect information.Finally,the C2f module in the feature fusion network is replaced with a BoT(bottleneck transformer)structure,and the multi-head self-attention mechanism in the Transformer is fused with convolution to enhance the model's global position information perception ability.The experimental results show that the proposed algorithm achieves mean average precision(mAP)of 80.5%on the NEU-DET dataset,which is five percentage points higher than the original YOLOv8 algorithm.At the same time,the detection speed reaches 83 frames per second,meeting the requirements of real-time detection.
关键词
热轧带钢/表面缺陷/目标检测/深度学习Key words
hot-rolled strip steel/surface defect/object detection/deep learning引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据