Revealing Invisible

RESUMEN: El proyecto Revealing Invisible, perteneciente al Observatorio Tecnológico HP 2020/21, tiene como objetivo magnificar los cambios aparentemente imperceptibles en secuencias de vídeo. Se abordó el problema utilizando redes neuronales convolucionales (CNNs), utilizando librerías de alto nivel como PyTorch y OpenCV. La propuesta se basa en que la CNN, se entrena con dos imágenes de entrada (original y original ligeramente magnificada), para dar como salida una imagen de salida magnificada (groundtruth). Para ello, no se parte de un conjunto de imágenes de entrenamiento y su respectivo conjunto de validación (imágenes magnificadas), sino que éstas se generan en caliente, de manera dinámica (producción sintética del dataset). En definitiva, el procesado de imágenes comprende los siguientes pasos: realizar una copia de la imagen original, extraer una ventana aleatoria de la misma, aumentar su tamaño en un factor de escala y pegar dicha ventana magnificada en la copia, de manera centrada respecto a la posición de extracción. Dicho pegado se realizará mediante el método de alpha blending, tomando como coeficiente el valor de la gaussiana. Por ende, las únicas diferencias entre la imagen ligeramente aumentada (entrada) y la de groundtruth, serían el factor de escala y el valor de la gaussiana en el borde. Por tanto, la red neuronal propuesta debe aprender a magnificar dichos cambios imperceptibles a partir del método de procesado explicado previamente. Es decir, se entrenará y validará con imágenes, y cuando se aplique a vídeos, las secuencias de vídeo se dividirán en imágenes (frames), se pasarán por la red y se aunarán, para formar el vídeo magnificado. En relación a la optimización de la red, se probarán distintas arquitecturas: capas convolucionales (ConvNets), con bloques residuales (ResNets) y bloque denso (DenseNets). A su vez, para cada arquitectura se ensayarán distintos algoritmos de optimización del descenso por gradiente: SGD, RMSprop y Adam. La evaluación de resultados, se realizará en base a la métrica de validación Mean Square Error (MSE), además de un estudio de las curvas de entrenamiento y validación. Tras escoger la estructura de la red, se confeccionará el estudio de distintos hiperparámetros: learning rate, número de capas y weight decay. La decisión de la arquitectura, algoritmo de optimización e hiperparámetros óptimos, se llevará a cabo en base al estudio teórico realizado inicialmente, teniendo en cuenta criterios como overfitting, vanishing gradient, degradación de la función de coste o métricas de validación. Además, en el modelo final propuesto también se aplicarán métricas de calidad de imagen (image quality assessment) como PSNR, SSIM o MS SSIM. Finalmente, el modelo definitivo se entrenará para un mayor número de épocas y se utilizará en la magnificación de secuencias de vídeos.ABSTRACT: The Revealing Invisible project, part of the HP Technology Observatory 2020/21, aims to magnify seemingly imperceptible changes in video sequences. The problem was approached using convolutional neural networks (CNNs), utilising high-level libraries such as PyTorch and OpenCV. The proposal is based on the fact that the CNN is trained with two input images (original and slightly magnified original), to produce as output a magnified image (groundtruth). For this, we do not start from a set of training images and their respective validation set (magnified images), but these are generated dynamically (synthetic production of the dataset). In short, image processing comprises the following steps: make a copy of the original image, extract a random window from it, increase its size by a scale factor and paste the magnified window into the copy in a way that is centered with respect to the extraction position. This pasting will be done using the method of alpha blending, taking as a coefficient the value of the Gaussian. Therefore, the only differences between the slightly enlarged image (input) and the groundtruth image would be the scale factor and the value of the Gaussian at the edge. Therefore, the proposed neural network must learn to magnify such imperceptible changes from the processing method explained previously. That is, it will be trained and validated with images, while when applied to videos, the video sequences will be divided into images (frames), passed through the network and joined together, to form the magnified video. In relation to the network optimization, different architectures will be tested: convolutional layers (ConvNets), with residual blocks (ResNets) and dense block (DenseNets). In turn, for each architecture, different gradient descent optimization algorithms will be tested: SGD, RMSprop and Adam. The evaluation of results will be carried out based on the validation metric Mean Square Error (MSE), in addition to a study of the training and validation curves. After choosing the network structure, the study of different hyperparameters will be carried out: learning rate, number of layers and weight decay. The decision of the optimal architecture, optimization algorithm and hyperparameters will be made based on the initial theoretical study, taking into account criteria such as overfitting, vanishing gradient, cost function degradation or validation metrics. In addition, image quality metrics (image quality assessment) such as PSNR, SSIM or MS-SSIM will also be applied in the proposed final model. Finally, the final model will be trained for a larger number of epochs and will be used in the magnification of video sequences.Máster en Ciencia de Dato

Machine learning algorithms for monitoring pavement performance

ABSTRACT: This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods

Consultorio contable : Universidad Tecnológica de Bolívar /

Los estudiantes de contaduría pública de la universidad tecnológica de bolívar con este proyecto tendrán la posibilidad de practicar en la realidad los conocimientos adquiridos en el aula de clase. Adicional a esto la universidad estará brindando un apoyo social a todas las empresas que requieran asesorías en el mejoramiento de sus procesos operacional generando de esta manera beneficios para la sociedad en general. Son estas las principales razones por las que planteamos el proyecto del consultorio contable, aquí encontraremos una iniciativa de competencia para los contadores públicos de de la universidad tecnológica de bolívar.Incluye bibliografí

Mountain strongholds for woody angiosperms during the Late Pleistocene in SE Iberia

Mediterranean mountains played an essential role during glacial periods as vegetation refugia. The SE Iberia Late Pleistocene woody angiosperm fossil and floristic evidences are reviewed in the context of phylogeographical studies aiming to identify (i) spatial patterns related to woody angiosperms glacial survival, (ii) structural and functional characteristics of montane refugia, and (iii) gaps in knowledge on the woody angiosperm patterns of survival in Mediterranean mountains. The distribution of palaeobotanical data for SE Iberia refugia has been found to be taphonomically biased due to the scarcity of available and/or studied high-altitude Late Pleistocene sites. However, Siles Lake data together with floristic inference provide evidences for woody angiosperms’ survival in a high-altitude Mediterranean area. The main features boosting survival at montane contexts are physiographic complexity and water availability. Phylogeography studies have mainly been conducted at a continental scale. Although they cohere with palaeobotanical data to a broad scale, a general lack of sampling of SE Iberian range-edge populations, as well as misconceptions about the origin of the populations sampled, impede to infer the proper location of woody angiosperms’ mountain refugia and their importance in the post-glacial European colonisation. We conclude that floristic, geobotanical, palaeobotanical, ethnographical and genetic evidence should be merged to gain a deeper understanding on the role played by Mediterranean mountains as glacial refugia in order to explain the current distribution of many plants and the large biodiversity levels encountered in Mediterranean mountain areas. This is hallmark for effective and efficient conservation and management

Mosquitonet

Mosquitonet is an open-source pavement distress dataset collected with a low-cost vehicle-mounted system for pavement distress detection through Deep Learning algorithms. There are 7099 images with 13 distress classes, annotated by experts in 12 formats: COCO, PASCAL VOC, YOLO/YOLOv4-v7 PyTorch, TF Object Detection, TFRecord, YOLOv3 Keras, Retinanet Keras and CreateML. The images were gathered by means of a low-cost, high-resolution and fast-acquisition system, Mosquito. The Mosquito system is an unmanned aerial vehicle mounted on a 3D-printed structure that is attached to a vehicle using a suction cup system. The Mosquito system provides images and GPS coordinates per image for speeds up to 120 km/h, where its remote control allows the co-pilot to adjust its parameters in real time for better capture. Annotations for training Deep Learning models were performed manually by pavement experts

Revelando lo invisible

[EN] The Revealing Invisible project, part of the HP Technology Observatory 2020/21, aims to magnify seemingly imperceptible changes in video sequences. The problem was approached using convolutional neural networks (CNNs), utilising high-level libraries such as PyTorch and OpenCV. The proposal is based on the fact that the CNN is trained with two input images (original and slightly magnified original), to produce as output a magnified image (groundtruth). For this, we do not start from a set of training images and their respective validation set (magnified images), but these are generated dynamically (synthetic production of the dataset). In short, image processing comprises the following steps: make a copy of the original image, extract a random window from it, increase its size by a scale factor and paste the magnified window into the copy in a way that is centered with respect to the extraction position. This pasting will be done using the method of alpha blending, taking as a coefficient the value of the Gaussian. Therefore, the only differences between the slightly enlarged image (input) and the groundtruth image would be the scale factor and the value of the Gaussian at the edge. Therefore, the proposed neural network must learn to magnify such imperceptible changes from the processing method explained previously. That is, it will be trained and validated with images, while when applied to videos, the video sequences will be divided into images (frames), passed through the network and joined together, to form the magnified video. In relation to the network optimization, different architectures will be tested: convolutional layers (ConvNets), with residual blocks (ResNets) and dense block (DenseNets). In turn, for each architecture, different gradient descent optimization algorithms will be tested: SGD, RMSprop and Adam. The evaluation of results will be carried out based on the validation metric Mean Square Error (MSE), in addition to a study of the training and validation curves. After choosing the network structure, the study of different hyperparameters will be carried out: learning rate, number of layers and weight decay. The decision of the optimal architecture, optimization algorithm and hyperparameters will be made based on the initial theoretical study, taking into account criteria such as overfitting, vanishing gradient, cost function degradation or validation metrics. In addition, image quality metrics (image quality assessment) such as PSNR, SSIM or MS-SSIM will also be applied in the proposed final model. Finally, the final model will be trained for a larger number of epochs and will be used in the magnification of video sequences.[ES] El proyecto Revealing Invisible, perteneciente al Observatorio Tecnológico HP 2020/21, tiene como objetivo magnificar los cambios aparentemente imperceptibles en secuencias de vídeo. Se abordó el problema utilizando redes neuronales convolucionales (CNNs), utilizando librerías de alto nivel como PyTorch y OpenCV. La propuesta se basa en que la CNN, se entrena con dos imágenes de entrada (original y original ligeramente magnificada), para dar como salida una imagen de salida magnificada (groundtruth). Para ello, no se parte de un conjunto de imágenes de entrenamiento y su respectivo conjunto de validación (imágenes magnificadas), sino que éstas se generan en caliente, de manera dinámica (producción sintética del dataset). En definitiva, el procesado de imágenes comprende los siguientes pasos: realizar una copia de la imagen original, extraer una ventana aleatoria de la misma, aumentar su tamaño en un factor de escala y pegar dicha ventana magnificada en la copia, de manera centrada respecto a la posición de extracción. Dicho pegado se realizará mediante el método de alpha blending, tomando como coeficiente el valor de la gaussiana. Por ende, las únicas diferencias entre la imagen ligeramente aumentada (entrada) y la de groundtruth, serían el factor de escala y el valor de la gaussiana en el borde. Por tanto, la red neuronal propuesta debe aprender a magnificar dichos cambios imperceptibles a partir del método de procesado explicado previamente. Es decir, se entrenará y validará con imágenes, y cuando se aplique a vídeos, las secuencias de vídeo se dividirán en imágenes (frames), se pasaran por la red y se aunaran, para formar el vídeo magnificado. En relación a la optimización de la red, se probaran distintas arquitecturas: capas convolucionales (ConvNets), con bloques residuales (ResNets) y bloque denso (DenseNets). A su vez, para cada arquitectura se ensayarán distintos algoritmos de optimización del descenso por gradiente: SGD, RMSprop y Adam. La evaluación de resultados, se realizará en base a la métrica de validación Mean Square Error (MSE), además de un estudio de las curvas de entrenamiento y validación. Tras escoger la estructura de la red, se confeccionará el estudio de distintos hiperparámetros: learning rate, número de capas y weight decay. La decisión de la arquitectura, algoritmo de optimización e hiperparámetros óptimos, se llevará a cabo en base al estudio teórico realizado inicialmente, teniendo en cuenta criterios como overfitting, vanishing gradient, degradación de la función de coste o métricas de validación. Además, en el modelo final propuesto también se aplicarán métricas de calidad de imagen (image quality assessment) como PSNR, SSIM o MS-SSIM. Finalmente, el modelo definitivo se entrenará para un mayor número de épocas y se utilizará en la magnificación de secuencias de vídeos.Peer reviewe

Improving detection of asphalt distresses with deep learning-based diffusion model for intelligent road maintenance

Research on road infrastructure structural health monitoring is critical due to the increasing problem of deteriorated conditions. The traditional approach to pavement distress detection relies on human-based visual recognition, a time-consuming and labor-intensive method. While Deep Learning-based computer vision systems are the most promising approach, they face the challenges of reduced performance due to the scarcity of labeled data due, high annotation costs misaligned with engineering applications, and limited instances of minority defects. This paper introduces a novel generative diffusion model for data augmentation, creating synthetic images of rare defects. It also investigates methods to enhance image quality and reduce production time. Compared to Generative Adversarial Networks, the optimal configuration excels in reliability, quality and diversity. After incorporating synthetic images into the training of our pavement distress detector, YOLOv5, its mean average precision has been enhanced. This computer-aided system enhances recognition and labelling efficiency, promoting intelligent maintenance and repairs