# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
## 1 Introduction
It can lessen the impact of human factors on the detection results and, at the same time, it can realize automated inspections on a large scale and in relation to continuous production lines, so it is often used for inspection of actual production. Advantages of the automated inspection process include the improvement of production efficiency, the ability to handle a large amount of image data, and the ability to distinguish production lines. However, this method also has a lot of defects, namely the classification of defect types is relatively poor, it requires manual extraction of the characteristics of various defects, and the precision of defect detection is relatively poor. Therefore, deep-learning-based industrial defect detection technologies have a broad prospect in our intelligent manufacturing sector, which needs our continuous exploration and innovation. The advantage of deep learning is that, by simply inputting specific data, the computer can automatically recognize various data and extract features, avoiding the disadvantage of manually extracting features. Currently, deep-learning-driven target recognition techniques are advancing rapidly. These target detection techniques for deep learning [1] methods are primarily categorized into the following two types. The main features of the one-stage methods are a good performance, a faster speed, and ease of practical application. The main one-stage target detection methods are YOLO [2] and SSD [3]. The two-stage detection methods need to generate the required region in advance and then the network classifies the corresponding region and obtains the prediction information. Typical representatives of two-stage algorithms include U-net [4], R-CNN [5], fast R-CNN [6], RetinaNet [7], faster R-CNN [8], and mask R-CNN [9]. These algorithms first generate the corresponding alternative regions in stage one, followed by classification and bounding box adjustment of these regions in stage two to arrive at the final recognition result. In addition, there are transformer [10; 11; 12; 13] and unsupervised learning [14; 15] approaches that have been brought to the computer vision field to solve the related problems in target detection. There have been a lot of scholars who have studied the application of algorithms deriving from deep learning target detection of steel surface defects. [PERSON] [16] proposed that the information based on the model should incorporate the ECA-SimSPPFSIoU-Yolov5 effective channel attention to enhance the significant weights. [PERSON] et al. [17] proposeda small sample steel plate defect detection algorithm based on lightweight YOLOv8 to bypass the difficult-to-apply deep learning approach when it comes to the detection of small sample defects. The LMSRNet network was designed to replace the YOLOv8 backbone, and the CBFPN and ECSA modules were developed to address the issue of the light weight of the model. [PERSON] et al. [18] proposed an algorithm based on the improved MSFT-YOLO model, integrating the TRANS module into the network and data enhancement processing to increase the accuracy of steel defect detection. [PERSON] et al. [19] proposed MCB-FAH-YOLOv8 to address the problems of defect detection, such as false detection and high leakage rate, by adding the improved convolutional attention mechanism module (CBAM) and the replaceable four-head ASFF predictor, so that the network could enhance the ability to detect tiny objects and dense targets and improve the accuracy at the expense of speed. The above methods are all based on YOLO for steel surface defects. [PERSON] et al. [20] proposed a method for detecting steel using multiscale lightweight attention, using a channel attention module for multilevel feature maps to reconstruct their channel-related information and improve the detection effect. [PERSON] et al.