Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 12 of 12 for “"VGG16"”.
-
Identificação de ativos de linha de transmissão por imagens de drones
… Rede Neural Convolucional (CNN); e a arquitetura VGG16 utilizando a técnica de transfer learning. Os resultados demonstraram que, embora a VGG16 tenha obtido a maior acurácia absoluta, o modelo deWavelet Scattering com Redes Neurais apresentou o melhor equilíbrio entre precisão e custo …
-
Automated Visual Inspection of Lyophilized Products via Deep Learning and Autoencoders
… learning neural network architectures including VGG16 and ResNet50. We compare results from training these architectures from scratch to results using fine-tuning of pretrained variants of the models, and find that the pretrained variants not only help improve accuracies, but help the model learn …
-
Satellite Image Analysis and Sidewalk Classification using Deep Learning Models
… tasks using pretrained CNN models including VGG16 and ResNet50. I extended these models by adding custom layers at the top of pretrained layers, employing various techniques to improve the classification accuracy.</p> <p>The dataset comprises 4,731 images of sidewalk based on occlusion …
-
Sensitivity of Feedforward Neural Networks to Harsh Computing Environments
… performance to be on par with random guessing. VGG16, ResNet50, and InceptionV3 were also tested for their robustness. ResNet50 and InceptionV3 were both more robust than VGG16. This could be due to their use of Batch Normalization or the fact that ResNet50 and InceptionV3 both use shortcut …
-
Parkinson's Disease Automated Hand Tremor Analysis from Spiral Images
… using extracted features from the ResNet-50 and VGG16 pre-trained deep network models. The VGG16 extracted features achieve 95.4% accuracy classifying images drawn by people with Parkinson’s Disease. The extracted features of both methods were also used to develop a tremor severity rating system …
-
Deep Learning for Early Detection, Identification, and Spatiotemporal Monitoring of Plant Diseases Using Multispectral Aerial Imagery
… deep learning convolutional neural networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) in classifying crop diseases for apples, grapes, and tomatoes. The results of the study show that the best performing crop-disease classification models were those trained on the VGG16 network, …
-
Multi-Modal Ocular Recognition in presence of occlusion in Mobile Devices
… descriptors (HOG, and GIST) with the deep VGG16 descriptor for eyebrow-based user authentication. The second scheme was eyeglass-based user authentication which consisting of eyeglasses segmentation, morphological cleanup, features extraction, and learning-based matching. The best result of …
-
Effective Features and Machine Learning Methods for Human Recognition Based on Multi-biometric Systems
… Convolutional Neural Networks (GoogleNET, VGG16, ResNET). An implementation of a person identification system fusing different combinations of biometric modalities; face, ear, eye, hand, and palmprint at score level has been examined. Comparison of these combinations has been employed to …
-
Identification of undesirable behaviour in CCTV footage using Deep Learning
… the results of using a pre-trained CNN (VGG16) and Autoencoder for purpose of feature extraction. Being unsupervised, the system requires very little human interference and it was designed to teach itself how to differentiate an anomaly from a non-anomalous event. The only human input …
-
A Machine Learning and Computer Assisted Methodology for Diagnosing Chronic Lower Back Pain on Lumbar Spine Magnetic Resonance Images
… segmentation algorithm. The network consists of VGG16 layers pre-trained using a subset of non-medical images from the ImageNet database and fine-tuned using the training portion of the ground-truth dataset. The results of these experiments show the accurate delineation of important boundaries of …
-
The Detection of Vegetation Species in Remote Sensing Imaging
… deep convolutional neural networks (Google-NET, VGG16, VGG19, ResNET50, ResNET101) and shallow convolutional neural networks have been trained to five different classes of ROI images including unknown range: wheat, black grass, road, and bushes. Finally, the conclusions drawn from this research …
-
Parameter reduction in deep learning and classification
The goal of this thesis is to develop methods to reduce model and problem complexity in the area of classification tasks. Whether it is a traditional or a deep learning classification task, decreasing complexity helps to greatly improve efficiency, and also adds regularization to the models. In …