206 research outputs found
Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters
Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable
Automatic vision based fault detection on electricity transmission components using very highresolution
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations.
Fault identification is one of the most significant bottlenecks faced by Electricity transmission and
distribution utilities in developing countries to deliver credible services to customers and ensure
proper asset audit and management for network optimization and load forecasting. This is due to
data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and
general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial
resolution to monitor four major Electric power transmission network (EPTN) components
condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks
(CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage
object detection model on the electric transmission power line imagery to localize, classify
and inspect faults present. The components fault considered include the broken insulator plate,
missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based
on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth
to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation
performed the best with a mean Average Precision of 89.61%. All the developed SSD based
models achieve a high precision rate and low recall rate in detecting the faulty components, thus
achieving acceptable balance levels F1-score and representation. Finally, comparable to other
works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection
and their component fault mapping in the long - run if these deep learning architectures are widely
understood, adequate training samples exist to represent multiple fault characteristics; and the
effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale
datasets are clearly understood
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
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
Machine Learning in Sensors and Imaging
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens
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