# Leather Defect Detection Based on Improved YOLOv8 Model
## 1 Introduction  
Leather plays a crucial role in the industry as a key raw material widely used in the manufacture of products such as clothing, bags, and footwear. Ensuring the high quality of leather materials is essential. In this context, leather defect detection is particularly important as it directly influences the production quality and efficiency of leather products, crucial for meeting high standards. Through accurate defect detection, problems in the production process are detected and eliminated in a timely manner, thereby enhancing the overall quality of the final product and market competitiveness. However, current research in leather defect detection struggles to meet the requirements for accuracy and speed in detection. Therefore, proposing an accurate, reliable, and fast intelligent detection algorithm is the current focus of research. In the field of leather defect detection, the initial approach primarily relied on manual defect detection and marking. However, this method encountered several issues, including inconsistencies in detection results due to varying inspector skills, the absence of standardized criteria, and the subjectivity and fatigue experienced by inspectors [1]. These issues not only hinder the efficiency and quality of leather production but also increase enterprise costs associated with training and employing high-level inspectors. In order to overcome the drawbacks, researchers have started to introduce new automated methods to replace manual testing, largely utilizing machine learning methods based on image analysis and deep learning techniques [2]. These include texture-based methods [3; 4; 5], wavelet-based methods [6; 7; 8], and classifier-based methods [9]. However, these methods are evaluated on small test sets and rely on human-designed features, whichmay not ensure robustness on highly variable real-world leather images. Additionally, the uncertainties in leather images, such as morphology and contrast, as well as variability in defects both between and within images, prevent simultaneous detection of multiple defects with traditional machine learning methods. Deep learning technology automatically learns features to effectively accomplish defect target detection and classification, thus making it the optimal solution for leather defect detection. Therefore, domestic and foreign researchers have proposed solutions based on deep neural network models in recent years. For example, [PERSON] et al. (2019) introduced a leather defect detection method based on ViT (vision transformer), leveraging low-resolution images for anomaly detection and classification. The proposed method surpasses current lightweight techniques in classification accuracy, with minimal computational requirements and reduced dependency on image resolution and training sample size. [PERSON] et al. (2019) proposed a robust leather defect detection method using trainable guided attention, integrating an optimized backbone network with a Feature Pyramid Network and attention mechanism to accurately detect and classify leather defects of various sizes and characteristics. This approach introduces cross-channel guidance and spatial attention, enhancing its applicability in real-time industrial inspection environments and achieving a validation accuracy of 93.89%. [PERSON] et al. (2019) introduced a novel framework based on parameter-optimized residual networks for automatic classification of leather surface defects. They utilized an ultra-high-definition imaging system to capture leather surface images, optimized image dataset size and sliding patch window size using the least-squares method, addressed gradient vanishing issues in deep learning with residual modules, and achieved automatic defect classification with a classification accuracy of 94.6%. However, the above studies focus on the accuracy of the model and the speed of real-time detection, respectively, while in the industrial environment, the speed and accuracy of leather defect detection are equally important. In this context, [PERSON] et al.