# Steel Surface Defect Detection Based on YOLOv8-TLC
## 1 Introduction  
In recent years, many researchers have used machine vision and image processing technologies to detect objects in images. By identifying the types of objects and determining their locations within the image, they achieve classification and localization of various targets. However, traditional object detection methods are easily influenced by the detection environment. While they can perform well under specific conditions, in real production environments, factors like lighting and object orientation can affect the accuracy and speed of detection. Moreover, traditional methods rely on manually designed features, requiring extensive prior knowledge and cumbersome procedures. This process is not only complex but also computationally heavy, making it unsuitable for real-time detection. Since metal surface defects are usually detected on production lines to manage defective products, there is a high demand for real-time detection, which traditional object detection algorithms struggle to meet. With the rapid development of deep learning, object detection technology has made significant progress. Deep learning, by simulating the way the human brain processes information, can automatically learn and extract features from images, enabling efficient object recognition and localization. This approach not only enhances the accuracy and robustness of algorithms but also simplifies the detection process, reducing reliance on prior knowledge. Using deep learning for object detection offers numerous advantages. In the early days, [PERSON] et al. proposed AlexNet [1] and achieved remarkable success in the ImageNet (ILSVRC) competition, marking a major milestone for deep learning in computer vision tasks and shifting the focus of object detection towards deep learning techniques. Object detection methods based on deep learning are mainly divided into two branches: two-stage detectors and one-stage detectors. Two-stage detectors first generate candidate object regions through a region proposal network, followed by fine classification and location adjustments of these regions. This approach achieves high accuracy but at the cost of speed. In contrast, one-stage detectors adopt an end-to-end approach, directlypredicting object categories and locations across the entire image, offering superior speed, and making them more suitable for real-time object detection. By integrating deep learning with region proposal techniques, RCNN [2; 3] emerged as a significant approach. This method converts the object detection task into a region-based classification problem, offering simplicity and scalability. However, due to frequent convolution operations, it suffers from substantial redundant computations, limiting its performance. [PERSON] et al. further improved RCNN by introducing Fast RCNN [4], which enhanced detection accuracy and sped up the detection process, though the time-consuming region extraction issue remained unsolved. Building upon this, [PERSON] et al. introduced Faster RCNN [5], significantly improving model accuracy, but the issue of inconsistent region proposal box sizes in the region proposal network still persists. Single-stage object detectors directly use deep convolutional neural networks on the raw image to predict the locations and classes of objects, eliminating the need for a region proposal step, which significantly increases detection efficiency. To address the high computational complexity, slow speed, and difficulties in handling different scales and targets associated with traditional object detection methods, [PERSON] et al. introduced the SSD [6] model, which employs multi-scale features and data augmentation techniques to better adapt to objects of varying sizes, further enhancing the generalization capability of object detection. To continue improving the efficiency of object detection tasks, [PERSON] et al. proposed YOLO [7; 8] at CVPR 2016. Subsequently, they introduced Anchor Boxes and additional convolutional layers, resulting in YOLOv2 and YOLO9000 [9].