# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
These defects not only affect the performance of the steel strip but also diminish the overall quality of the product [4]. Furthermore, surface defects can lead to more serious issues, such as belt breakage and accumulation, resulting in significant losses in steel production [5]. Therefore, rapid and accurate detection of surface defects in steel strips is crucial for improving product quality and production efficiency, making it an important area of research in related technologies [6]. With the continuous advancement of automation technology, traditional manual inspection methods have gradually been replaced by advanced computer vision technologies and machine learning (ML) algorithms. The application of machine learning, particularly in the fields of image processing and data analysis, has introduced novel solutions for the detection of surface defects in steel strips. Traditional machine vision methods mainly extract texture features from a local or global perspective to distinguish defects from the background [7; 8]. For example, [PERSON], [PERSON] [9] et al., whose research was based on full differential image decomposition to detect various defects on texture homogeneous surfaces for tiny defects under low contrast conditions. [PERSON], [PERSON] [10] et al. explored DWT attributes and evaluated the explicit definitions of the spectral coefficients of the DWT, carrying out task-oriented subset correlation statistical analyses to generate a defect detection criterion to evaluate the multifaceted nature of the defects in the texture image. The literature [11] proposes an improved SVD phase space reconstruction algorithm by combining the nonlinear dynamics feature recognition theory and evaluating and comparing the experimental results to prove the feasibility of the proposed method. However, these image-processing-based methods face significant noise interference in industrial applications, which may lead to poor adaptability and robustness. In recent years, machine learning-based methods have been successfully applied to many automated defect detection tasks. For instance, [PERSON] et al. [12] proposed a deep learning-based method for detecting surface defects on steel strips, which automatically classifies surface defects using convolutional neural networks (CNN), achieving high classification accuracy and robustness. [PERSON] et al. [13] proposed a surface defect detection method for steel strips based on deep convolutional neural networks (CNN). The study successfully detected various common defects by applying data augmentation to the steel strip surface images and combining CNN for feature extraction and classification. The literature [14] explores the use of convolutional neural networks (CNNs) for real-time detection of steel surface defects. The research demonstrates how deep learning technologies can replace traditional defect detection methods, leading to better efficiency and fewer false positives in industrial settings. In addition, transfer learning has been widely applied in the field of steel strip defect detection. Transfer learning leverages models trained on other domains, such as bridge defect detection [15], and can fully exploit their advantages in steel strip surface defect detection. This approach allows for the effective enhancement of model detection capabilities, even when the dataset is insufficient. For example, [PERSON] et al. [16] demonstrated that transfer learning could significantly improve the accuracy and generalization ability of steel strip surface defect detection models, especially in scenarios with limited data. With the continuous development of science and technology, deep learning has achieved remarkable progress in strip surface defect detection, addressing the robustness challenges often encountered by traditional machine learning algorithms [17]. By leveraging their ability to capture complex patterns in large datasets, deep learning models have proven particularly effective for defect detection on steel strip surfaces.