# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 4 Conclusions  
To address the challenges of detecting small and densely distributed book signature marks, which often result in high rates of false and missed detections, we have introduced BSMD-YOLOv8, a specialized object detection model designed specifically for this particular task. By incorporating the Lightweight Multi-scale Residual Network (LMRNet) as an alternative backbone to YOLOv8n, we have achieved a more streamlined network architecture while significantly enhancing the accuracy of signature mark detection. Additionally, the introduction of the Low-GD and High-GD modules has improved the multi-scale feature fusion capabilities of the neck, leading to better integration of local and global features and further boosting recognition accuracy. The adoption of the WIoU loss function in place of the CIoU loss function has contributed to the model's enhanced generalization performance, particularly in detecting low-quality book signature marks. Experimental results have demonstrated the exceptional performance of the BSMD-YOLOv8 algorithm model on our self-built dataset. Compared to the original YOLOv8n model, it has achieved a substantial 65% reduction in network parameters, a notable seven frames per second increase in FPS, and significant improvements in accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively. Furthermore, it has surpassed the performance of other currently available mainstream algorithm models. Although the BSMD-YOLOv8 model has effectively addressed the issues of false and missed detections in book signature mark detection and possesses a compact network size and rapid detection speed that make it well-suited for practical applications, there are still opportunities for further enhancement. Specifically, reducing the model's floating-point operations and further refining its accuracy in identifying smaller and more densely packed targets are areas of focus. Future research endeavors could delve into optimizing the model's architecture to elevate its performance, broadening its applicability to encompass more intricate scenarios, enhancing its real-time performance and efficiency, and integrating additional technologies or methodologies to propel the ongoing advancement and deployment of signature sequence detection technology within bookbinding processes. Conceptualization, Q.Y., L.G. and L.W. ; methodology, L.G. and L.W. ; software, L.G. and L.W. ; validation, L.G., Q.Y., X.X. and L.W. ; formal analysis, Q.Y. and X.X. ; investigation, L.G. and L.W. ; resources, Q.Y. and X.X. ; data curation, L.G., L.W. and Q.Y. ; writing--original draft preparation, L.G. and L.W. ; writing--review and editing, L.G., Q.Y. and L.W. ; visualization, L.G. and L.W. ; supervision, Q.Y. and X.X. ; project administration, Q.Y. ; funding acquisition, Q.Y. and X.X. All authors have read and agreed to the published version of the manuscript. Figure 12: Comparison of detection results between YOLOv8_L_I and BSMD-YOLOv8: (a) YOLOv8_L_I (using CIoU); (b) BSMD-YOLOv8 (using WIoU). **Funding:** This research was funded by the Guilin Major Special Project (20220103-1), the Guangxi Science and Technology Base and Talent Special Project (Gui Ke AD24010012), the Guangxi Key Research and Development Plan (Gui Ke AB23026105), and the Science and Technology Innovation Base Construction Class (Gui Ke ZY21195030). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the authors due to data underlying the results used in this paper are not publicly available at this time.