# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 1 Introduction
To address this problem, we propose a new backbone, the Lightweight Multi-scale Residual Network (LMRNet), which achieves a lightweight network design while enhancing the accuracy of signature mark detection. The neck component in YOLOv8 serves the purpose of fusing features at multiple scales. To achieve this, the design integrates elements from both the Feature Pyramid Network (FPN) [16] and the Path Aggregation Network (PANet) [17] in its neck design. However, when it comes to integrating information across layers, the current method of  
Figure 1: Examples of mismatched detections and accurate detections. information fusion struggles to transmit information without loss, leading to insufficient local features of signature marks in the deep network. To address this issue, we have designed the Low-GD and High-GD modules to enhance the neck's multi-scale feature fusion capabilities, thereby improving the fusion of local and global features of signature marks. In YOLOv8, the head component employs a decoupled architecture, where distinct branches are dedicated to object classification and bounding box regression tasks. The bounding box loss functions are crafted to precisely position objects by imposing penalties for any discrepancies observed between the predicted bounding boxes and the actual ground truth boxes. However, CIoU, which is used in YOLOv8, still exhibits certain limitations when dealing with low-quality samples in the dataset. To address this limitation, we introduce WIoU as a replacement for CIoU. WIoU prioritizes anchor boxes with moderate quality and mitigates the impact of harmful gradients arising from low-quality signature marks, ultimately leading to an improvement in the model's overall performance. In summary, we propose BSMD-YOLOv8, a novel and efficient approach for accurate book signature mark detection. Our method introduces a new backbone, the Lightweight Multi-scale Residual Network (LMRNet), which enhances the accuracy of small object detection while maintaining a lightweight network design. Additionally, we design Low-GD and High-GD modules to facilitate the integration of local and global features of signature marks. To reduce the impact of harmful gradients from low-quality examples, we replace the CIoU loss function with WIoU loss. Experimental results demonstrate that, compared to YOLOv8n, BSMD-YOLOv8 reduces the number of parameters by 65%, increases the frame rate by 7 FPS, and achieves improvements in accuracy, recall, and mAP50 by 2.2%, 8.6%, and 3.9% respectively, highlighting its capability for rapid and accurate detection of signature marks. The rest of this paper is organized as follows. Section 2 offers an introduction to the dataset used in our study, accompanied by a statistical analysis. It also introduces the overall architecture of the BSMD-YOLOv8 model, details the proposed enhancements to YOLOv8, describes the model training environment, and outlines the evaluation metrics utilized in our research. In Section 3, we report the experimental results and perform a thorough evaluation, including comparisons with other leading one-stage models and ablation studies to evaluate the enhancements proposed in this paper. Lastly, Section 4 summarizes the key findings of the study.