Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s

Abstract

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.