# Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n
## 5 Conclusions  
In this paper, a series of improvement measures are proposed based on the YOLOv8n baseline network; the goal is to improve the precision and efficiency of fabric surface defect detection while reducing computational complexity and the occurrence of missed detection and one false detection and improving the confidence level of the model detection task. Firstly, introduce the SPPF_LSKA module into the backbone network. This mechanism effectively reduces the interference of complex patterns and different colors on defect  
Figure 14: YOLOv8n detection results. Figure 15: Improved YOLOv8n detection results. detection by enhancing the ability to focus on a wide range of features on the fabric surface. Making the network more focused on key feature regions related to defects. Secondly, the CARAFE module is used to replace the traditional upsampling operation. The CARAFE module can adaptively learn the correlation between each pixel in the feature map, significantly reducing information loss during the feature recombination process and thus more accurately restoring high-resolution feature maps. This feature enhancement strategy helps the network capture more detailed defect information, further improving detection performance. In addition, a small target detection layer specifically designed for the P2 feature layer is added. This improvement aims to enhance the detection capabilities of small defect targets, effectively reducing the false detection and missed detection rates of minor flaws during the detection process. Finally, OREPA technology is introduced into the C2f module to optimize the computational efficiency of the model through structural reparameterization. The introduction of OREPA not only significantly reduces the computational and time costs during the training process but also effectively accelerates the convergence speed of the model, further improving the efficiency and stability of the model during the training phase. Through the above series of improvement measures, while retaining the lightweight advantage of the original model, efficient detection of fabric defects has been achieved. The experimental results show that the precision, recall, and [EMAIL_ADDRESS] of the model on self-built datasets can reach 96.3%, 91.2%, and 96.6%, respectively. The significance of this study lies in proposing an effective improvement scheme and verifying its practicality in seamless fabric defect detection. Future research will focus on the practical application and performance improvement of the YOLOv8n model in seamless fabric defect detection. Although this paper has achieved good experimental results on the self-built dataset, in real production environments, there may be complex lighting conditions, background interference, and variations in different materials and patterns on the fabric surface. Therefore, future research can explore how to further enhance the robustness of the model in real production environments. In addition, the diversity of the dataset is also a key issue. In the future, by expanding the size and coverage of the dataset to include more types of fabric materials and defect samples, the impact of data bias on model performance can be reduced. Although OREPA technology is introduced to reduce computing costs, further model compression and acceleration techniques such as knowledge distillation, quantization (FP16/INT8), and pruning can still be used in the future to further reduce computational complexity so that the model can achieve real-time detection on resource-constrained edge devices such as industrial cameras or embedded devices like Jeston Nano. Meanwhile, in terms of industrial applications, future research on improving the YOLOv8n model should also focus on the deployment of a complete seamless fabric defect detection system, including automated assembly line integration from data collection and real-time detection to defect labeling and alarm feedback.