## 1 Introduction  
Steel is an essential and important material for national construction with widespread applications, particularly in infrastructure development, where it plays an irreplaceable role. Currently, the majority of steel materials are produced through pressure processing of raw materials to obtain materials with specific shapes and properties. During the process of pressure processing, steel is inevitably subject to defects due to factors such as cost constraints, equipment limitations, existing technologies, and surface roughness [1]. These defects may include cracks, inclusions, patches, pitting, and other imperfections [2]. Once these defects appear in the steel, its ability to withstand compression, tension, corrosion, and other factors will inevitably be affected to varying degrees. Therefore, defect detection is particularly important before the production of steel materials. Currently, defect detection methods can be broadly categorized into three types: traditional defect detection, machine vision-based defect detection, and deep learning-based defect detection. Traditional defect detection methods mainly contain manual sampling [3], infrared detection [4], and magnetic flux leakage detection [5]. The artificial sampling method randomly chooses and tests the sample with the naked eye, but there are sampling imbalances, resulting in large errors. This is highly susceptible to human factors and other issues. The infrared detection method detects defects through temperature changescaused by different surfaces of the steel material, which is limited to the infrared absorption capacity of steel and is unable to accurately classify defects. Magnetic flux leakage detection tests defects by detecting the presence of magnetic features on defective steel surfaces. This technique is not capable of accurate identification of fine and narrow cracks, thus limiting the types of defects which can be detected. Machine vision inspection technology is an improvement to traditional inspection which reduces staff workload and is not affected by the environment. Machine vision-based defect detection methods are mainly classified into four categories: defect detection based on image preprocessing, the classifier, feature extraction, and image segmentation. Machine vision inspection methods have made tremendous progress in the task of recognizing steel surface defects, which are more efficient and practical than traditional defect detection methods. However, they generally have problems such as poor classification of multiple defect types, the need for manual extraction of features, and performing well in real time but having poor accuracy. Therefore, deep learning-based defect detection technology has emerged as an important research direction. Deep learning inputs data and automatically extracts features, not only retaining the advantages of no manual labor and avoiding an environmental impact but also eliminating the need to manually extract features. This end-to-end modeling architecture simplifies the industrial production process. The mainstream deep learning target detection algorithms include two-stage [6] methods such as R-CNN [7], Fast R-CNN [8], Faster R-CNN [9], and Mask R-CNN [10], and one-stage [11] methods like SSD [12], the YOLO [13] algorithm, and object detection algorithms based on the Transformer architecture, like DETR [14]. The two-stage target detection algorithm has a relatively high detection accuracy, but it detects targets more slowly than the one-stage target detection algorithm because it generates a series of candidate frames during the detection process. The one-stage target detection algorithm turns them into a regression problem. [PERSON] et al. [15] proposed a Faster R-CNN algorithm based on deformable convolution and multi-scale fusion. While their proposed model performed well in detecting surface defects on steel, it struggled to meet industrial requirements in terms of detection speed.