# An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8  
[PERSON]  
1 School of Software Engineering, Anyang Normal University, Anyang 455000, China; [EMAIL_ADDRESS] (Q.Z. ); (20231@aynu. of traditional methods, [PERSON] [5] used a method based on connected components for oracle bone script detection, but this method is not very effective regarding complex backgrounds or noisy oracle bone rubbing images. As computer technology continues to evolve, object detection algorithms rooted in deep learning have emerged as the prevailing trend. In recent years, many experts and scholars, both domestically and internationally, have dedicated significant effort toward researching OBI detection by using deep learning techniques. OBI detection algorithms rooted in deep learning can be primarily categorized into two types: two-stage and one-stage detection algorithms. Of these, YOLO [6] and SSD [7] fall under the umbrella of one-stage detection algorithms, and Faster R-CNN [8], RFBNet [9], etc., are two-stage detection algorithms. RefineDet [10] is an improvement based on the SSD algorithm and effectively blends the strengths of both types. Although two-stage detection algorithms have higher accuracy, their implementation is complex and computationally intensive, the inference speed is slow, and they perform poorly when detecting small and dense objects. Conversely, one-stage detection algorithms are fast, accurate, and widely applicable. In research on one-stage detection algorithms, the YOLO series is used extensively across a range of diverse scenarios because of its excellent performance; these algorithms are lightweight, have a low background false-alarm rate, show good performance in detecting small objects, have strong model generalization abilities, and have a fast detection speed. For instance, YOLO can be used to detect objects in pictures taken by unmanned aerial vehicles [11], for traffic sign recognition [12], for steel surface defect detection [13], etc. [PERSON] [14] at Ritsumeikan University in Japan enhanced the SSD algorithm to improve the detection accuracy of small-font oracle bone characters. [PERSON] [15] constructed an OBI detection dataset and analyzed the detection performance of representative general detection frameworks (comprising Faster R-CNN, SSD, RefineDet, RFBnet, and YOLOv3 [16]) applied to this dataset, thus improving the best-performing YOLOv3, achieving an F-measure of 80.1%. [PERSON] [17] proposed a multi-scale Gaussian kernel-based oracle bone detector. [PERSON] [18] proposed a pseudo-label-based oracle bone script detection architecture. While the algorithm demonstrated promising performance on the training set, its detection accuracy was suboptimal on the validation and test sets. While these algorithms have enhanced the performance of object detection to a certain degree, there is still room for further refinement in terms of detection accuracy. According to research results in the literature [15], YOLOv3 performs well among mainstream deep learning models. YOLOv8 is the latest version (as of 1 January 2024) of the YOLO series, released by Ultralytics in 2023. YOLOv8 adopts a more efficient network structure and more advanced algorithm technology, enhancing the precision and rapidity of object detection even further. YOLOv8 includes multiple models at different scales, and YOLOv8_n stands out as the most compact and efficient model within this series, offering the quickest performance among its counterparts. In conclusion, to further improve the precision of detection, the present study focused on the uneven target size and significant noise of OBI rubbing images as well as real-time detection. Using the YOLOv8_n model as a baseline, our primary advancements were as follows:  
1. Given the concentration of oracle bone script detection data, some oracle bone characters in certain images are small. To enhance detection performance for small objects, a detection head with a scale of 160 \(\times\) 160 was added;
2.