Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on InceptionV3, VGG16 and ResNet50V2, which implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we obtain a better understanding of the performance of the algorithms in the classification process.

The digitalization of historical documents is of interest for many reasons, including historical preservation, accessibility, and searchability. One of the main challenges with the digitization of old newspapers involves complex layout analysis, where the content types of the document must be determined. In this context, this paper presents an evaluation of the most recent YOLO methods for the analysis of historical document layouts. Initially, a new dataset called BHN was created and made available, standing out as the first dataset of historical Brazilian newspapers for layout detection. The experiments were held using the YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. For training, validation, and testing of the models, the following historical newspaper datasets were combined: BHN, GBN, and Printed BlaLet GT. Recall, precision, and mean average precision (mAP) were used to evaluate the performance of the models. The results indicate that the best performer was YOLOv8, with a ${\mathbf{Recall}}_{\mathbf{test}}$ of $\mathbf{81}\%$ and an ${\mathbf{mAP}}_{\mathbf{test}}$ of $\mathbf{89}\%$. This paper provides insights on the advantages of these models in historical document layout detection and also promotes improvement of document image conversion into editable and accessible formats.

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.

Coffee cultivation is vital to the global economy, but faces significant challenges with diseases such as rust, miner, phoma, and cercospora, which impact production and sustainable crop management. In this scenario, deep learning techniques have shown promise for the early identification of these diseases, enabling more efficient monitoring. This paper proposes an approach for detecting diseases and pests on coffee leaves using an efficient single-shot object-detection algorithm. The experiments were conducted using the YOLOv8, YOLOv9, YOLOv10 and YOLOv11 versions, including their variations. The BRACOL dataset, annotated by an expert, was used in the experiments to guarantee the quality of the annotations and the reliability of the trained models. The evaluation of the models included quantitative and qualitative analyses, considering the mAP, F1-Score, and recall metrics. In the analyses, YOLOv8s stands out as the most effective, with a mAP of 54.5%, an inference time of 11.4 ms and the best qualitative predictions, making it ideal for real-time applications.

High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2–7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder–decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model.

The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. The evaluation of the proposal is conducted through quantitative, qualitative, and computational cost analyses based on the number of parameters and FLOPs. Regarding the qualitative analysis, a comparison between the models was performed using samples that present reconstruction challenges. The proposed model achieves a PSNR-$\mu$ of 43.51 dB and SSIM-$\mu$ of 0.9917, achieving competitive quality metrics comparable to HDR-GAN while reducing the computational cost by 6× in FLOPs and 7× in the number of parameters, using approximately half the GPU memory consumption, demonstrating an effective balance between visual fidelity and efficiency.