## 1 Introduction  
Steel is a mainstay of our daily lives. It is employed in various industries, such as construction, automotive, and aviation, and it often comprises materials directly related to the safety of many people, making quality control in the steel manufacturing process a critical challenge. Steel surface defects can be caused by a variety of factors, including temperature variations during the manufacturing process, pressure imbalances, and the introduction of impurities. These can result in defects such as microscopic cracks, scratches, inclusions, and rolled-in scale on the steel surface. These defects directly affect the physical properties and durability of the product, and if they are not detected early enough in the continuous steel manufacturing process and introduced into downstream processes, they can lead to costly problems and, in severe cases, process accidents [1]. With the advent of Industry 4.0, automation is rapidly proliferating throughout the manufacturing sector. The steel industry is also embracing this shift and is actively lever aging deep learning-based algorithms to enhance the detection of surface defects during production [2]. Deep learning techniques for detecting steel surface defects predominantly fall into three categories: classification, object detection, and object segmentation. For instance, [PERSON] et al. [3] developed a method employing the Hough transform and deep learning to classify surface defects and identify defect types. [PERSON] et al. [4] explored the automated detection of steel strip surface defects using generative adversarial networks (GANs) and one-class classifiers (OCCs). To precisely locate defects, [PERSON] et al. [5] proposed the optimized lightweight network WearNet for surface defect detection, minimizing the parameters of the network layers while maximizing scratch detection performance, achieving 99.67% accuracy on the NEU-DET dataset. In particular, the Grad-CAM technique was utilized to visualize the areas that most influence the prediction, supporting decision-making in classifying the severity of defects. [PERSON] et al. [6] employed faster R-CNN and the VGG feature extractor, and surface defects on objects with light-reflecting properties were detected with high accuracy. Specifically, highly reflective surfaces, such as mirrors, were effectively identified by the faster R-CNN model and the approach yielded superior results compared to traditional methods. Previous research has effectively identified approximate defect locations, yet challenges remain in accurately determining the exact shape or size of defects. Defects in steel products can significantly affect the subsequent processing, depending on their shape and extent. Precise information about the size and location of defects is crucial to facilitate defect follow-up. Therefore, among the three main defect detection techniques--classification, object detection, and segmentation--segmentation remains the most suitable method for analyzing defects on steel surfaces. Precisely identifying the location and size of defects is a valuable approach that can greatly enhance the efficiency of the manufacturing process. Nonetheless, a point to bear in mind with segmentation approaches is that they demand highly precise annotations to differentiate between healthy and defective regions, and gathering such annotation data is not only time-consuming but also costly. In fact, compared to bounding box annotation in object detection, segmentation annotation can be up to 15 times more time-consuming and also more expensive [7], necessitating highly skilled human experts. To address these issues, this paper proposes an efficient framework that utilizes ResNet-50 to classify images from the steel manufacturing process into defect-free and defective categories, generates bounding box annotations using faster R-CNN on the defective images, and then trains a segmentation model using only the bounding boxes as lower-level labels.