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Convolutional Neural Networks for embedded vision / Lucas Fernandez Brillet (2020)  

Public Titre : Convolutional Neural Networks for embedded vision Titre original : Réseaux de neurones CNN pour la vision embarquée Type de document : Thèse/HDR Auteurs : Lucas Fernandez Brillet, Auteur ; Stéphane Mancini, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2020 Importance : 164 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, Spécialité : Mathématiques, sciences et technologies de l’information, informatique Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compression d'image [Termes IGN] détection d'objet [Termes IGN] image à haute résolution [Termes IGN] instrument embarqué [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Index. décimale : THESE Thèses et HDR Résumé : (auteur) Recently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy Note de contenu : Introduction 1- Deep learning overview 2- Methodology to adapt the computational complexity of CNN-based object detection for efficient inference in an applicative use-case 3- CNN compression 4- Cascaded and compressed CNNs for fast and lightweight face detection 5- Hardware evaluation on embedded multiprocessor Thesis Conclusion & Perspectives Numéro de notice : 28392 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques, sciences et technologies de l’information, informatique : Grenoble : 2020 DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03101523/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98739