# DBCW-YOLO: A Modified YOLOv5 for the Detection of Steel Surface Defects
## 1 Introduction  
Steel is an important raw material that plays an important role in industrial manufacturing. Therefore, ensuring steel quality is a crucial and demanding task. During the steel manufacturing process, the production environment and processing equipment limitations can result in various surface defects on the product, such as cracks, scratches, plaques, punches, indentations, and other imperfections. These defects can affect both the aesthetics and quality of steel [1]. The detection of defects on the surface of steel is, therefore, an essential part of industrial production. The earliest method of defect detection was manual visual inspection. However, the traditional manual visual inspection method suffers from high subjectivity and empirical variation. This may limit the reliability of the test results. In addition to this, manual inspection is inefficient and costly, which limits the further development of traditional manual visual inspection methods. As machine learning continues to advance, defect detection methods on the basis of machine learning gradually replace manual detection methods. An adaptive method for detecting steel surface defects by exploiting the Haar wavelet transform was proposed by [PERSON] et al. [2] and was fruitful. [PERSON] et al. [3] used the features statistically derived from the magnitude spectrum obtained by Fourier transform for crack detection on the steel plate surface. In another approach, [PERSON] et al. [4] used Gabor filters for spatial and frequency domain defect detection in steel coils. These methods have made great strides compared to manual testing. However, machine learning methods require different analytical processing for specific images, resulting in poor robustness and suboptimal detection accuracy using machine learning defect detection. Over the past few years, deep learning has achieved considerable advances in flaw detection thanks to its powerful learning capabilities [5; 6]. Deep learning-based object detection methods are mainly classified into one- and two-stage methods. A two-stage model, such as the R-CNN series [7; 8; 9], follows a two-step model, first generating candidate regions and then classifying them after refining their positions. The two-stage detection method performs well on the detection error rate and missed detection rate. However, the speed of detection is relatively slow, and it is not able to achieve the requirements of real-time detection. Therefore, one-stage detection methods have emerged. The widely used one-stage object detection models at this stage include SSD [10], YOLO series [11; 12; 13; 14], and Retina Net [15]. One-stage target detection algorithms have gained popularity in target detection applications that require efficient and real-time performance due to their fast detection speed, end-to-end training, fewer hyperparameters, applicability to multiple tasks, excellent small target detection capability, and better real-time performance. YOLO series algorithms are more representative algorithms inside one-stage object detection, but the accuracy of one-stage target detection algorithms is insufficient. While target detection algorithms have shown high accuracy in detecting defects with small-scale variations, they still perform poorly in detecting targets with large-scale variations. Most current target detection algorithms rely heavily on the prediction of feature mappings that provide limited information about multi-scale targets. Therefore, detailed image information needs to be utilized wisely. For example, deep networks can capture more comprehensive semantic information. However, they may be less suitable for detecting defects in ground resolution. Therefore, the performance of feature extraction networks in capturing multi-scale features is of particular interest. Moreover, for the problem of target feature loss, we can conclude that the improvement of the detection head is essential. YOLO still fails to detect complex defects with sufficient accuracy.