###### Abstract  
Stamps are an essential mechanism for authenticating documents in various sectors and institutions. Given the high volume of documents and the increase in forgery, it is necessary to adopt automated methods to identify stamps on documents. In this context, techniques based on deep learning stand out as an efficient solution for automating this process. To this end, this article presents a performance evaluation of YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s in detecting stamps on scanned documents. To train, validate, and test the models, an adapted dataset with 732 images from the combination of the StaVer and DDI-100 datasets is used. The performance of the models is evaluated by means of quantitative and qualitative analyses and by analyzing the computational cost. The results show that, in terms of performance, the YOLOv9s model obtained the best result, with a mAP (Mean Average Precision) of 98.7% for a precision and recall of 97.6%. In terms of computational cost and shorter inference time, the YOLOv11s model stands out. This comparative approach is a contribution to the state of the art for implementation in automatic stamp authentication devices. s +
Footnote †: journal: Applied sciences  
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Object detection models are classified into Two-Stage and One-Stage Detectors [6]. The first type, for example, Faster R-CNN [7], generates region proposals before performing detection and classification, while the second type, such as YOLO [8] (You Only Look Once), performs these processes in a single step, providing greater speed. Thus, for applications requiring real-time detection, One-Stage Detectors become the more efficient option. YOLO models outperform not only Two-Stage Detectors like Faster R-CNN, but also other One-Stage models such as SSD [9] (Single Shot MultiBox Detector) and EfficientDet [10] in various applications [11; 12; 13; 14; 15]. In comparison, YOLO is significantly faster, enabling real-time applications with minimal loss of accuracy. Therefore, YOLO models stand out as a powerful option for object detection. The YOLO architecture was first introduced by [PERSON] in 2016, and it was designed for real-time object detection in images and videos. Since then, several versions of YOLO have been developed. YOLOv8 [16], released in January 2023 by Ultralytics founder [PERSON], supports multiple computer vision tasks, including object detection, segmentation, pose estimation, tracking, and classification. In February 2024, YOLOv9 [17] was introduced, incorporating the GELAN (Generalized Efficient Layer Aggregation Network) architecture and the PGI (Programmable Gradient Information) concept. Just three months later, YOLOv10 [18] was launched, with its primary innovation being the elimination of dependence on Non-Maximum Suppression (NMS), a technique heavily relied upon by its predecessors. Building on the advancements of previous YOLO versions, YOLOv11 was announced in October 2024, introducing significant architectural improvements that enhanced speed and efficiency without compromising accuracy [19]. In this context, this paper presents a performance evaluation of four state-of-the-art YOLO models--YOLOv8, YOLOv9, YOLOv10, and YOLOv11--in their small versions for stamp detection in scanned documents. The models are trained using the StaVer (Stamp Verification) [20] and DDI-100 (Distorted Document Images) [21] datasets. To evaluate the performance of deep learning-based models, a quantitative analysis is conducted using precision, recall, mAP, and computational cost metrics, such as model complexity and speed. Additionally, a qualitative evaluation is performed based on the bounding boxes predicted by the models and their confidence levels. Furthermore, the ability of the best-performing model to detect stamps in the presence of image distortions is analyzed. In summary, the main contributions of this research are as follows:  
* Based on the combination and annotation of the StaVer and DDI-100 datasets, an adapted dataset was created.