Inspecção Visual de Isoladores Eléctricos -Abordagem baseada em Deep Learning

To supply the electrical population’s demand is necessary to have a good quality power distribution systems. Electrical asset inspection, like electrical towers, dam or power line is a high risk and expensive task. Nowadays it is done with traditional methods like using a helicopter equipped with several sensors or with specialised human labour. In the last years, the Unmanned Aerial Vehicle (UAV) exponential growth (most common called drones) make them very accessible for different applications. They are cheaper and easy to adapt. Adopting this technology will be in the future the next step on electrical asset inspection. It will provide a better service (safer, faster and cheaper), particularly in power line distribution. This thesis brings forward an alternative to traditional methods using a UAV for images processing during the insulator visual inspection. The developed work implement real-time insulators visual detection using na Artificial Neural Network (ANN), You Only Look Once (YOLO) in this case, on medium and high voltage power lines. YOLO was trained with different types and sizes of insulators. Isn’t always possible to see what the UAV is recording so it has a gimbal system which controls the camera orientation/position. It will centre the insulator on the image and this way getting a better view of it. All the training and tests were performed on board Jetson TX2.A inspeção de ativos elétricos, sejam eles torres elétricas, barragens ou linhas elétricas, é realizada com recurso a helicópteros, equipados com sensores para o efeito ou, de uma forma mais minuciosa, com o recurso a mão-de-obra especializada. Ambas as situações são trabalhos de risco elevado. Nos últimos anos temos assistido a um enorme crescimento de veículos aéreos não tripulados, vulgarmente chamados de drones. Estes sistemas estão bastante desenvolvidos e são economicamente acessíveis, o que os torna perfeitos para variadíssimas funções. A inspeção de linhas elétricas não ´e exceção. Esta dissertação, pretende ser uma primeira abordagem `a utilização de drones para uma inspeção autónoma de linhas elétricas, nomeadamente no processamento de imagem para inspeção visual de isoladores. O trabalho desenvolvido, consiste na implementação de um sistema que funciona em tempo real para a deteção visual de isoladores. A deteção ´e feita com recurso a uma rede neuronal, neste caso específico a fico a You Only Look Once (YOLO), que foi treinada com isoladores de diferentes tamanhos e materiais. Uma vez que nem sempre ´e possível acompanhar o que está a ser filmado, o drone consta de um sistema capaz de orientar a câmara, chamado gimbal, para centrar o isolador na imagem e assim conseguir obter um melhor enquadramento do ativo a ser inspecionado. Todos este desenvolvimentos e consequentes testes foram realizados com a utilização de processamento paralelo, que neste caso foi utilizada a placa Jetson TX2

Efficient video collection association using geometry-aware Bag-of-Iconics representations

Abstract Recent years have witnessed the dramatic evolution in visual data volume and processing capabilities. For example, technical advances have enabled 3D modeling from large-scale crowdsourced photo collections. Compared to static image datasets, exploration and exploitation of Internet video collections are still largely unsolved. To address this challenge, we first propose to represent video contents using a histogram representation of iconic imagery attained from relevant visual datasets. We then develop a data-driven framework for a fully unsupervised extraction of such representations. Our novel Bag-of-Iconics (BoI) representation efficiently analyzes individual videos within a large-scale video collection. We demonstrate our proposed BoI representation with two novel applications: (1) finding video sequences connecting adjacent landmarks and aligning reconstructed 3D models and (2) retrieving geometrically relevant clips from video collections. Results on crowdsourced datasets illustrate the efficiency and effectiveness of our proposed Bag-of-Iconics representation

3D Mesh Simplification. A survey of algorithms and CAD model simplification tests

Simplification of highly detailed CAD models is an important step when CAD models are visualized or by other means utilized in augmented reality applications. Without simplification, CAD models may cause severe processing and storage is- sues especially in mobile devices. In addition, simplified models may have other advantages like better visual clarity or improved reliability when used for visual pose tracking. The geometry of CAD models is invariably presented in form of a 3D mesh. In this paper, we survey mesh simplification algorithms in general and focus especially to algorithms that can be used to simplify CAD models. We test some commonly known algorithms with real world CAD data and characterize some new CAD related simplification algorithms that have not been surveyed in previous mesh simplification reviews.Siirretty Doriast

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered