## 2 Related Work  
### Steel Surface Defect Detection  
The steel manufacturing process is primarily categorized into integrated and compact processes, with large-scale steel manufacturing plants typically adopting the integrated process. This process involves ironmaking, steelmaking, continuous casting, and hot rolling, as illustrated in Figure 1. The ironmaking process combines iron ore and compounds to produce steel composed of iron and carbon, which is then melted in a furnace. Subsequently, the molten steel undergoes steelmaking to remove impurities before progressing to the continuous casting process. During continuous casting, molds are used to form intermediate shapes, leading to the hard characteristics of steel. Finally, the steel is hot rolled into shapes such as billets, blooms, and slabs, during which various defects may occur, detected by an inline camera for inspection. The objective of steel surface defect detection is to classify and localize defects within an image. Traditional methods primarily include Sobel [8], scale-invariant feature transform (SIFT) [9], Fourier transform [10], histogram of oriented gradient (HOG) [11], and local binary pattern (LBP) [12], combined with classifiers to differentiate defects from the background. However, these methods are limited in their capacity to extract features from images varying in complexity and scale. In response, recent advancements have seen the adoption of deep learning algorithms for surface defect detection, which offer enhanced detection performance and accuracy despite the complexity of images. Deep learning models for steel flaw detection are categorized into one-stage and two-stage models based on their structural differences. One-stage models encompass the You Only Look Once (YOLO) series and the single-shot multi-box detector (SSD). Previous studies have introduced techniques such as multi-feature fusion and adaptive anchor adjustment to enhance the somewhat limited fault detection accuracy of one-stage models. For instance, [PERSON] et al. improved YOLOX by integrating a parallel fusion network structure and a self-adjusting label assignment algorithm, addressing the varied semantic hierarchy of defects in images and dynamic changes during model training, significantly enhancing defect detection accuracy [13]. The RAF-SSD model incorporates the channel attention mechanism of [14] with multi-feature fusion, markedly increasing the detection accuracy for steel surface defects compared to the traditional SSD model and achieving superior accuracy to two-stage models. The model performed well for certain defect types, yet it did not outperform the two-stage model across all defect categories. Figure 1: Process of steel manufacturing. These models boast rapid inference speeds, supporting real-time object detection; however, they generally exhibit lower detection accuracy compared to the two-stage model. [PERSON] et al. (2015) enhanced the Faster RCNN network by incorporating multi-scale feature fusion and induced anchor RPNs. This improvement addressed challenges such as micro-defect detection on textured and uneven solar cell surfaces, markedly enhancing the defect detection speed compared to the existing fast R-CNN. To detect defects on steel strip surfaces in real-time, [PERSON] et al. (2016) substituted the convolutional layers in the faster R-CNN with depthwise separable convolutions, significantly cutting the inference time per image from 0.2 s to 0.05 s. Nevertheless, this speed is still slower than the YOLO model, which achieves an impressive processing speed of 0.012 s.  
To summarize, the one-stage model is well-suited for applications that demand high speed and real-time processing, whereas the two-stage model is better for scenarios that require greater detection accuracy and precision. For this study, accuracy was prioritized to train a segmentation model using only bounding boxes, leading us to opt for the faster R-CNN model for object detection. Figure 2 shows a flowchart of the proposed framework.