This master's thesis evaluates data annotation, semantic segmentation and Docker for use in AWS. The data provided has to be annotated and is to be used as a dataset for the creation of a neural network. Different neural network models are then to be compared based on performance. AWS has the option to use Docker containers and thus that option is to be examined, and lastly the different tools available in AWS SageMaker will be analyzed for bringing a neural network to the cloud. Images were annotated in Ilastik and the dataset size is 276 images, then a neural network was created in PyTorch by using the library Segmentation Models PyTorch which gave the option of trying different models. This neural network was created in a notebook in Google Colab for a quick setup and easy testing. The dataset was then uploaded to AWS S3 and the notebook was brought from Colab to an AWS instance where the dataset then could be loaded from S3. A Docker container was created and packaged with the necessary packages and libraries as well as the training and inference code, to then be pushed to the ECR (Elastic Container Registry). This container could then be used to perform training jobs in SageMaker which resulted in a trained model stored in S3, and the hyperparameter tuning tool was also examined to get a better performing model. The two different deployment methods in SageMaker was then investigated to understand the entire machine learning solution. The images annotated in Ilastik were deemed sufficient as the neural network results were satisfactory. The neural network created was able to use all of the models accessible from Segmentation Models PyTorch which enabled a lot of options. By using a Docker container all of the tools available in SageMaker could be used with the created neural network packaged in the container and pushed to the ECR. Training jobs were run in SageMaker by using the container to get a trained model which could be saved to AWS S3. Hyperparameter tuning was used and got better results than the manually tested parameters which resulted in the best neural network produced. The model that was deemed the best was Unet++ in combination with the Dpn98 encoder. The two different deployment methods in SageMaker was explored and is believed to be beneficial in different ways and thus has to be reconsidered for each project. By analysis the cloud solution was deemed to be the better alternative compared to an in-house solution, in all three aspects measured, which was price, performance and scalability.

Autonomous Vehicles need precise information as to the Drive-able space in order to be able to safely navigate. In recent years deep learning and Semantic Segmentation have attracted intense research. It is a highly advancing and rapidly evolving field that continues to provide excellent results. Research has shown that deep learning is emerging as a powerful tool in many applications. The aim of this study is to develop a deep learning system to estimate the Free Drive-able space. Building on the state of the art deep learning techniques, semantic segmentation will be used to replace the need for highly accurate maps, that are expensive to license. Free Drive-able space is defined as the drive-able space on the correct side of the road, that can be reached without a collision with another road user or pedestrian. A state of the art deep network will be trained with a custom data-set in order to learn complex driving decisions. Motivated by good results, further deep learning techniques will be applied to measure distance from monocular images. The findings demonstrate the power of deep learning techniques in complex driving decisions. The results also indicate the economic and technical feasibility of semantic segmentation over expensive high definition maps.

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Autonomous vehicles perform semantic segmentation to orient themselves, but training neural networks for semantic segmentation requires large amounts of labeled data. A hand-labeled real-life dataset requires considerable effort to create, so we instead turn to virtual simulators where the segmented labels are known to generate large datasets virtually for free. This work investigates how effective synthetic datasets are in driving scenarios by collecting a dataset from a simulator and testing it against a real-life hand-labeled dataset. We show that we can get a model up and running faster by mixing synthetic and real-life data than traditional dataset collection methods and achieve close to baseline performance.

Despite recent advances in deep-learning based semantic segmentation, automatic building detection from remotely sensed imagery is still a challenging problem owing to large variability in the appearance of buildings across the globe. The errors occur mostly around the boundaries of the building footprints, in shadow areas, and when detecting buildings whose exterior surfaces have reflectivity properties that are very similar to those of the surrounding regions. To overcome these problems, we propose a generative adversarial network based segmentation framework with uncertainty attention unit and refinement module embedded in the generator. The refinement module, composed of edge and reverse attention units, is designed to refine the predicted building map. The edge attention enhances the boundary features to estimate building boundaries with greater precision, and the reverse attention allows the network to explore the features missing in the previously estimated regions. The uncertainty attention unit assists the network in resolving uncertainties in classification. As a measure of the power of our approach, as of January 5, 2022, it ranks at the second place on DeepGlobe’s public leaderboard despite the fact that main focus of our approach — refinement of the building edges — does not align exactly with the metrics used for leaderboard rankings. Our overall F1-score on DeepGlobe’s challenging dataset is 0.745. We also report improvements on the previous-best results for the challenging INRIA Validation Dataset for which our network achieves an overall IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the official INRIA Test Dataset, our network scores 77.86% and 96.41% in overall IoU and accuracy. We have also improved upon the previous best results on two other datasets: For the WHU Building Dataset, our network achieves 92.27% IoU, 96.73% precision, 95.24% recall and 95.98% F1-score. And, finally, for the Massachusetts Buildings Dataset, our network achieves 96.19% relaxed IoU score and 98.03% relaxed F1-score over the previous best scores of 91.55% and 96.78% respectively, and in terms of non-relaxed F1 and IoU scores, our network outperforms the previous best scores by 2.77% and 3.89% respectively.

Environmental monitoring refers to a host of activities involving the sampling or sensing of diverse properties from an environment in an effort to monitor, study and overall better understand it. While potentially rich and scientifically valuable, these data often create challenging interpretation tasks because of their volume and complexity. This thesis explores the efficiency of Computer Vision-based frameworks towards the processing of large amounts of visual environmental monitoring data. While considering every potential type of visual environmental monitoring measurement is not possible, this thesis elects three data streams as representatives of diverse monitoring layouts: visual out-of-water stream, visual underwater stream and active acoustic underwater stream. Detailed structure, objectives, challenges, solutions and insights from each of them are presented and used to assess the feasibility of Computer Vision within the environmental monitoring context. This thesis starts by providing an in-depth analysis of the definition and goals of environmental monitoring, as well as the Computer Vision systems typically used in conjunction with it. The document continues by studying the visual out-of-water stream via the design of a novel system employing a contrast-guided approach towards the enhancement of low-light underwater images. This enhancement system outperforms multiple state-of-the-art methods, as supported by a group of commonly-employed metrics. A pair of detection frameworks capable of identifying schools of herring, salmon, hake and swarms of krill are also presented in this document. The inputs used in their development, echograms, are visual representations of acoustic backscatter data from echosounder instruments, thus contemplating the active acoustic underwater stream. These detectors use different Deep Learning paradigms to account for the unique challenges presented by each pelagic species. Specifically, the detection of krill and finfish is accomplish with a novel semantic segmentation network (U-MSAA-Net) capable of leveraging local and contextual information from feature maps of multiple scales. In order to explore the out-of-water visual data stream, we examine a large dataset composed by years-worth of images from a coastal region with strong marine vessels traffic, which has been associated with significant anthropogenic footprints upon marine environments. A novel system that involves ``traditional'' Computer Vision and Deep Learning is proposed for the identification of such vessels under diverse visual appearances on this monitoring imagery. Thorough experimentation shows that this system is able to efficiently detect vessels of diverse sizes, shapes, colors and levels of visibility. The results and reflections presented in this thesis reinforce the hypothesis that Computer Vision offers an extremely powerful set of methods for the automatic, accurate, time- and space-efficient interpretation of large amounts of visual environmental monitoring data, as detailed in the remainder of this work. / Graduate

Aujourd’hui, de nouvelles technologies permettent l’acquisition de scènes 3D volumineuses et précises sous la forme de nuages de points. Les nouvelles applications ouvertes par ces technologies, comme les véhicules autonomes ou la maintenance d'infrastructure, reposent sur un traitement efficace des nuages de points à grande échelle. Les méthodes d'apprentissage profond par convolution ne peuvent pas être utilisées directement avec des nuages de points. Dans le cas des images, les filtres convolutifs ont permis l’apprentissage de nouvelles représentations, jusqu’alors construites « à la main » dans les méthodes de vision par ordinateur plus anciennes. En suivant le même raisonnement, nous présentons dans cette thèse une étude des représentations construites « à la main » utilisées pour le traitement des nuages de points. Nous proposons ainsi plusieurs contributions, qui serviront de base à la conception d’une nouvelle représentation convolutive pour le traitement des nuages de points. Parmi elles, une nouvelle définition de voisinages sphériques multi-échelles, une comparaison avec les k plus proches voisins multi-échelles, une nouvelle stratégie d'apprentissage actif, la segmentation sémantique des nuages de points à grande échelle, et une étude de l'influence de la densité dans les représentations multi-échelles. En se basant sur ces contributions, nous introduisons la « Kernel Point Convolution » (KPConv), qui utilise des voisinages sphériques et un noyau défini par des points. Ces points jouent le même rôle que les pixels du noyau des convolutions en image. Nos réseaux convolutionnels surpassent les approches de segmentation sémantique de l’état de l’art dans presque toutes les situations. En plus de ces résultats probants, nous avons conçu KPConv avec une grande flexibilité et une version déformable. Pour conclure notre réflexion, nous proposons plusieurs éclairages sur les représentations que notre méthode est capable d'apprendre. / In the recent years, new technologies have allowed the acquisition of large and precise 3D scenes as point clouds. They have opened up new applications like self-driving vehicles or infrastructure monitoring that rely on efficient large scale point cloud processing. Convolutional deep learning methods cannot be directly used with point clouds. In the case of images, convolutional filters brought the ability to learn new representations, which were previously hand-crafted in older computer vision methods. Following the same line of thought, we present in this thesis a study of hand-crafted representations previously used for point cloud processing. We propose several contributions, to serve as basis for the design of a new convolutional representation for point cloud processing. They include a new definition of multiscale radius neighborhood, a comparison with multiscale k-nearest neighbors, a new active learning strategy, the semantic segmentation of large scale point clouds, and a study of the influence of density in multiscale representations. Following these contributions, we introduce the Kernel Point Convolution (KPConv), which uses radius neighborhoods and a set of kernel points to play the role of the kernel pixels in image convolution. Our convolutional networks outperform state-of-the-art semantic segmentation approaches in almost any situation. In addition to these strong results, we designed KPConv with a great flexibility and a deformable version. To conclude our argumentation, we propose several insights on the representations that our method is able to learn.

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The detection of cracks in sand moulds has long been a challenge for both safety and maintenance purposes. Traditional image processing techniques have been employed to identify and quantify these defects but have often proven to be inefficient, labour-intensive, and time-consuming. To address this issue, we sought to develop a more effective approach using deep learning techniques, specifically semantic segmentation. We initially examined three different architectures—U-Net, SegNet, and DeepCrack—to evaluate their performance in crack detection. Through testing and comparison, U-Net emerged as the most suitable choice for our project. To further enhance the model's accuracy, we combined U-Net with VGG-19, VGG-16, and ResNet architectures. However, these combinations did not yield the expected improvements in performance. Consequently, we introduced a new layer to the U-Net architecture, which significantly increased its accuracy and F1 score, making it more efficient for crack detection. Throughout the project, we conducted extensive comparisons between models to better understand the effects of various techniques such as batch normalization and dropout. To evaluate and compare the performance of the different models, we employed the loss function, accuracy, Adam optimizer, and F1 score as evaluation metrics. Some tables and figures explain the differences between models by using image comparison and evaluation metrics comparison; to show which model is better than the other. The conducted evaluations revealed that the U-Net architecture, when enhanced with an extra layer, proved superior to other models, demonstrating the highest scores and accuracy. This architecture has shown itself to be the most effective model for crack detection, thereby laying the foundation for a more cost-efficient and trustworthy approach to detecting and monitoring structural deficiencies.