基于双流YOLOv4的金属表面缺陷检测方法

Metal Surface Defect Detection Method Based on Dual-stream YOLOv4

目前有许多学者使用深度学习进行表面缺陷检测研究,由于这些研究大都沿用主流目标检测算法的思路,注重高级语义特征,而忽视了低级语义信息(色彩、形状)对表面缺陷检测的重要性,因此导致缺陷检测效果不够理想。为解决上述问题,提出了一种金属表面缺陷检测网络——双流YOLOv4网络,骨干网络分成两个分支,输入分为高分辨率图像和低分辨率图像,浅分支负责从高分辨率图像中提取低级特征,深分支负责从低分辨率图像中提取高级特征,通过削减两分支的层数和通道数来减少模型总参数量;为了强化低级语义特征,提出了一种树形多尺度融合方法(Tree-structured Multi-scale Feature Fusion Me-thod,TMFF),并设计了一个结合极化自注意力机制和空间金字塔池化的特征融合模块(Feature Fusion Module with Polarized Self-Attention Mechanism and Spatial Pyramid Pooling,FFM-PSASPP)应用到TMFF中。在东北大学热轧带表面缺陷数据集NEU-DET、金属表面缺陷数据集GC10-DET和伊莱特电饭煲内胆缺陷数据集Enaiter的测试集上对所提算法进行了测试,测得的map@50结果分别为0.80,0.66和0.57,相比大部分主流的用于缺陷检测的目标检测算法均有提升,且模型参数量仅为原YOLOv4的一半,速度与YOLOv4接近,可满足实际使用需求。

Currently,many researchers use deep learning for surface defect detection.However,most of these studies follow the mainstream object detection algorithm and focus on high-level semantic features while neglecting the importance of low-level semantic information(color,shape)for surface defect detection,resulting in unsatisfactory defect detection effect.To address this issue,a metal surface defect detection network called the dual-stream YOLOv4 network is proposed.The backbone network is split into two branches,with inputs consisting of high-resolution and low-resolution images.The shallow branch is responsible for extracting low-level features from the high-resolution image,while the deep branch is responsible for extracting high-level features from the low-resolution image.The model’s total parameter volume is reduced by cutting down the number of layers and channels in both branches.To enhance the low-level semantic features,a tree-structured multi-scale feature fusion method(TMFF)is proposed,and a feature fusion module with a polarized self-attention mechanism and spatial pyramid pooling(FFM-PSASPP)is designed and applied to the TMFF.The algorithm’s map@50 results on the test sets of the Northeastern University hot-rolled strip surface defect dataset(NEU-DET),the metal surface defect dataset(GC10-DET),and the enaiter rice cooker inner pot defect dataset are 0.80,0.66,and 0.57,respectively.Compared to most mainstream object detection algorithms used for defect detection,there is an improvement,and the model’s parameter volume is only half that of the original YOLOv4,with a speed close to YOLOv4,making it suitable for practical use.