Lu Jianbo, Zhu Mingrui, Ma Xiaoya, Wu Kunsheng
Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning 530001, China.
Department of Logistics Management and Engineering, Nanning Normal University, Nanning 530023, China.
Biomimetics (Basel). 2024 Jan 3;9(1):28. doi: 10.3390/biomimetics9010028.
Steel strip is an important raw material for the engineering, automotive, shipbuilding, and aerospace industries. However, during the production process, the surface of the steel strip is prone to cracks, pitting, and other defects that affect its appearance and performance. It is important to use machine vision technology to detect defects on the surface of a steel strip in order to improve its quality. To address the difficulties in classifying the fine-grained features of strip steel surface images and to improve the defect detection rate, we propose an improved YOLOv5s model called YOLOv5s-FPD (Fine Particle Detection). The SPPF-A (Spatial Pyramid Pooling Fast-Advance) module was constructed to adjust the spatial pyramid structure, and the ASFF (Adaptively Spatial Feature Fusion) and CARAFE (Content-Aware ReAssembly of FEatures) modules were introduced to improve the feature extraction and fusion capabilities of strip images. The CSBL (Convolutional Separable Bottleneck) module was also constructed, and the DCNv2 (Deformable ConvNets v2) module was introduced to improve the model's lightweight properties. The CBAM (Convolutional Block Attention Module) attention module is used to extract key and important information, further improving the model's feature extraction capability. Experimental results on the NEU_DET (NEU surface defect database) dataset show that YOLOv5s-FPD improves the mAP50 accuracy by 2.6% before data enhancement and 1.8% after SSIE (steel strip image enhancement) data enhancement, compared to the YOLOv5s prototype. It also improves the detection accuracy of all six defects in the dataset. Experimental results on the VOC2007 public dataset demonstrate that YOLOv5s-FPD improves the mAP50 accuracy by 4.6% before data enhancement, compared to the YOLOv5s prototype. Overall, these results confirm the validity and usefulness of the proposed model.
钢带是工程、汽车、造船和航空航天工业的重要原材料。然而，在生产过程中，钢带表面容易出现裂纹、麻点等缺陷，影响其外观和性能。利用机器视觉技术检测钢带表面缺陷以提高其质量具有重要意义。为了解决带钢表面图像细粒度特征分类困难以及提高缺陷检测率的问题，我们提出了一种改进的YOLOv5s模型，称为YOLOv5s-FPD（细粒度颗粒检测）。构建了SPPF-A（空间金字塔池化快速推进）模块来调整空间金字塔结构，并引入了ASFF（自适应空间特征融合）和CARAFE（特征内容感知重组）模块来提高带钢图像的特征提取和融合能力。还构建了CSBL（卷积可分离瓶颈）模块，并引入了DCNv2（可变形卷积网络v2）模块来提高模型的轻量化特性。使用CBAM（卷积块注意力模块）注意力模块提取关键和重要信息，进一步提高模型的特征提取能力。在NEU_DET（NEU表面缺陷数据库）数据集上的实验结果表明，与YOLOv5s原型相比，YOLOv5s-FPD在数据增强前将mAP50准确率提高了2.6%，在SSIE（钢带图像增强）数据增强后提高了1.8%。它还提高了数据集中所有六种缺陷的检测准确率。在VOC2007公共数据集上的实验结果表明，与YOLOv5s原型相比，YOLOv5s-FPD在数据增强前将mAP50准确率提高了4.6%。总体而言，这些结果证实了所提模型的有效性和实用性。
