# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 1 Introduction  
The rolled strip is widely used in national defense, aerospace, and other manufacturing industries and is a critical steel product. With the continuous improvement of scientific technology and industrial levels, the iron and steel manufacturing industry has made significant progress [1]. In the production process of strip steel, it will be affected by various factors, such as the performance of rolling equipment and fluctuations in the production process [2]. These factors may cause various defects on the surface of the strip, reduce the quality of the strip products, and cause severe economic losses. Therefore, it is necessary to carry out surface defect detection on strip steel. Traditionally, rolled steel defects are detected through manual visual inspection. This technique has low detection effectiveness, is easily influenced by the inspector's personality, and has the dangers of missed detection and false detection. As a result of the rapid advancement of computer vision technology, many image processing and traditional machine learning techniques are employed to ensure product quality. To identify and localize defects, the area of pixel mutation is identified in the image based on the color and shape features of the defects [3]. This form of detection is quick and inexpensive, making it a suitable alternative to artificial vision for detection, hence significantly enhancing the production efficiency of businesses. However, traditional machine learning techniques need human feature design and extraction; when surface imperfections vary, the algorithm must be modified, making it impossible for the traditional machine algorithm to fulfill the increasingly complex needs of industrial production [4]. Numerous deep learning-based techniques are employed for defect identification and classification due to the rapid development of convolutional neural networks [5]. There are two general categories of defect detection algorithms now in use: two-stage target detection algorithms and one-stage target detection algorithms. After feature extraction, a two-stage target detection algorithm creates a pre-selection box (region proposal) that might include the object to be detected. Following this, a sample classification calculation is made using a convolutional neural network, such as R-CNN [6], SPP-Net [7], Fast R-CNN [8], and Faster R-CNN [9]. One-stage target detection algorithms, such as OverFeat [10], YOLO [11], SSD [12], RetinaNet [13], and others, directly extract feature values from the network to classify and find targets. The deep learning technique automatically extracts features during picture recognition by using the image's pixel information as input. The created detection model can identify a range of faults after learning from a large number of samples, increasing the effectiveness and generalization of defect detection. Although deep learning models have excellent capabilities, model training requires powerful computer hardware. The weight files generated by training are typically several hundreds of megabytes, making deployment on devices with low resources challenging. Numerous lightweight networks, such as EfficientNet [14], MobileNet [15], ShuffleNet [16], and GhostNet [17], have been presented to reduce the number of model parameters and computations. Under the premise of preserving accuracy to the greatest extent feasible, these networks are optimized in terms of volume and speed, allowing them to achieve optimal performance and fulfill the needs of practical applications when processing power and storage space are restricted. Due to the reduction in the number of parameters and the quantity of computation, certain flaws may be overlooked during the feature extraction process of a lightweight network.