# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 1 Introduction  
In the field of bookbinding, the production of book signatures is a crucial step in the binding process, playing a vital role in ensuring the sequential order and completeness of book content. The signature mark, a special mark printed on the outer spine fold of each signature, serves as a visual inspection tool to verify the correctness of signature placement. When signatures are arranged in the correct order, these marks form a trapezoidal pattern, which becomes a key indicator for validating the accuracy of signature arrangement [1]. To prevent issues such as missing or extra signatures during the binding process, precise detection of signature mark positions is particularly important. This detection step enables the automated identification of binding errors, significantly enhancing production efficiency and quality control levels. Therefore, developing an accurate and efficient detection algorithm for signature marks is of paramount importance in improving the overall quality of bookbinding, further increasing production efficiency, and advancing the level of industrial automation. Traditional manual inspection methods are time-consuming and labor-intensive, and long-term visual tasks can easily lead to fatigue, increasing the likelihood of detection errors. Although detection methods based on traditional machine vision technology [2; 3; 4; 5; 6] have alleviated this issue to some extent, they rely on tedious manual feature engineering, resulting in lower efficiency and sensitivity to target occlusion, dense distribution, illumination changes, and scale variations. In contrast, deep learning-based object detection algorithms can automatically learn image features and exhibit stronger generalization capabilities, making them an effective solution for signature mark detection [7]. However, applying mainstream deep learning-based object detection algorithms directly to tasks involving small and densely packed targets, such as book signature marks, remains challenging, often resulting in missed detections and false positives, as illustrated in Figure 1. Research on signature mark detection has introduced diverse methodologies, including approaches based on an enhanced version of YOLOv5 [8]. This method substantially boosts the detection accuracy of signature marks by incorporating a small object detection layer. Additionally, reference [9] draws inspiration from two-stage object detection algorithms to propose a dual-core convolutional neural network, achieving notable improvements in detection precision. While these methods have achieved some improvements, they still face trade-offs between accuracy, speed, and model complexity. Object detection algorithms are mainly divided into two categories: two-stage detection algorithms such as RCNN [10] and Faster RCNN [11], and single-stage detection algorithms such as the YOLO series [12] and SSD [13]. While two-stage algorithms offer higher recognition accuracy, they are slower in detection speed; single-stage algorithms, with their significant speed advantage, are more suitable for real-time detection requirements. Among these, YOLOv8 stands out in terms of the speed-accuracy trade-off [14]. YOLOv8 is comprised of three primary elements: the backbone, neck, and detection head. Additionally, it offers a choice of five scaling factors (n, s, m, l, x) to tailor the model's size according to diverse application needs. YOLOv8's backbone is based on CSPDarknet53 [15], which employs five downsampling stages to extract features at different scales. However, considering that signature marks in images are small and densely distributed, the structure design of CSPDarknet53 leads to the loss of details from shallower layers due to extensive downsampling. Additionally, it extracts numerous redundant and unnecessary information in the feature maps. Both of these factors significantly hinder the accuracy of signature mark detection.