基于改进的YOLOv5s钢材表面缺陷检测研究

Research on steel surface defect detection based on improved YOLOv5s

针对在钢材表面缺陷检测任务中对缺陷特征提取能力不足以及特征融合不充分的问题,本文提出一种基于YOLOv5s改进的钢材表面缺陷检测算法YOLOv5-TBC。首先,在Backbone的核心特征提取模块中引入三重注意力机制(Triplet Attention),使Backbone更具有适应性和表征能力。其次,研究引入了加权双向特征金字塔网络(BiFPN),提升网络的特征融合,并引入了CBAM注意力机制优化模型对小尺度目标的检测能力。最后,添加了轻量级上采样算子CARAFE用来扩大模型的感受野,进一步提高对不同大小目标的检测效果。实验结果表明,改进后的YOLOv5s模型在NEU-DET数据集上的精确率(Precision,P)和平均准确率(mAP)分别为74.0%和76.6%,较YOLOv5s中P提升了5.8%,mAP提升了3.0%,较YOLOv7中P提升了3.8%,mAP提升了2.1%,证明该网络模型具有更良好的检测性能。

This paper proposes an improved steel surface defect detection algorithm YOLOv5-TBC based on YOLOv5s to address the issues of insufficient defect feature extraction capability and inadequate feature fusion in steel surface defect detection tasks.Firstly,a triplet attention mechanism is introduced into the core feature extraction module of the Backbone to enhance its adaptability and representational ability.Secondly,a weighted Bi-directional Feature Pyramid Network(BiFPN)is introduced to improve feature fusion,and a CBAM attention mechanism is introduced to optimize the model's detection capability for small-scale targets.Finally,a lightweight upsampling operator CARAFE is added to enlarge the model's receptive field,further improving the detection performance for targets of different sizes.Experimental results demonstrate that the improved YOLOv5s model achieves a precision(P)of 74.0% and a mean Average Precision(mAP)of 76.6% on the NEU-DET dataset,which is an increase of 5.8% in P and 3.0% in mAP compared to YOLOv5s,and an increase of 3.8% in P and 2.1%in mAP compared to YOLOv7,proving that the network model has better detection performance.