# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 2 Materials and Methods
### Methodology
#### 2.2.2 Improved PANet  
In image processing, the fusion of global and local features is a widely adopted and effective strategy [18; 19; 20]. Local features primarily focus on specific key points or regions within the image, such as the corners or edges of signature marks, and these fine details play a pivotal role in accurate signature mark recognition. On the other hand, global features capture the overall properties of the image, including the color distribution and shape contour of the book spine area. Given that signature marks are exclusively present in the book spine area, accurately pinpointing their positions heavily relies on these global features as well.  
Features at different levels carry positional information about objects of various sizes. Larger features encapsulate low-dimensional texture details and positions of smaller objects, while smaller features contain high-dimensional information and positions of larger objects. This suggests that the positional information of signature marks is present in larger features, whereas the positional information of book spines is present in smaller features. As illustrated in Figure 7a, despite its numerous paths and indirect interaction methods, PANet is only capable of fully integrating feature information from adjacent layers and cannot ensure lossless propagation of cross-layer feature information. This limitation  
Figure 6: Multi-scale residual convolution module structure diagram.  
restricts the amount of local feature information of signature marks that can be fused with the global feature information of the book spine area in the deep network, hindering the distinction between book signature marks and the background. Based on the Gather-and-Distribute Mechanism [21], this paper introduces Low-GD and High-GD modules aimed at enhancing the fusion of local and global features by incorporating multi-scale features extracted from the backbone into the deep network. The structure of the improved PANet is shown in Figure 7b.  
**Low-stage gather-and-distribute module.** The Low-GD module is employed to retain more low-level information by aggregating and fusing the B1, B2, and B4 features output from the backbone network, ultimately obtaining high-resolution features that preserve small target information. As depicted in Figure 8, the input multi-scale features are first aligned by downsampling the larger-scale B1 and B2 features through average pooling and upsampling the B4 feature through bilinear interpolation, adjusting them to the same scale as the N3 feature. The aligned features are then concatenated along the channel dimension and fused through PWConv. Following this, a Context Augmentation Module (CMA) [22] is utilized to capture spatial features of different receptive field sizes and model the differences between objects and backgrounds in greater detail. This module consists of 3\(\times\)3 depth-wise separable dilated convolutions (DilateDWConv) [23] with dilation rates of 1, 3, and 5. Finally, another PWConv is applied for fusion and output.  
**High-stage gather-and-distribute module.** In order to fuse global feature information more efficiently, we employ attention operations to fuse the information, As illustrated in Figure 9, this module aggregates and integrates the B2 and B3 features output from the backbone network. The B3 and B4 features are first downsampled through average  
Figure 8: Low-stage gather-and-distribute module structure diagram.  
Figure 7: PANet and Improved PANet structure diagram.  
pooling to obtain feature maps of the same scale as N4. Subsequently, the features are concatenated and merged using PWConv. Following this, an attention mechanism is employed to enhance the output by adaptively correlating global feature information. This attention module consists of channel attention [24] and spatial attention [25]. Finally, another PWConv is applied for the final output.