scholarly journals Classification of Metro Facilities with Deep Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Deqiang He ◽  
Zhou Jiang ◽  
Jiyong Chen ◽  
Jianren Liu ◽  
Jian Miao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Learning Technology ◽  
Deep Convolutional Neural Networks ◽  
Research Fields ◽  
Human Labor ◽  
Train Operation ◽  
The Common ◽  
Automatic Train Operation ◽  
Computational Resources

Metro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. In this paper, we evaluated the effectiveness in classifying the common facility images in metro tunnels based on Google’s Inception V3 DCNN. The model requires fewer computational resources. The number of parameters and the computational complexity are much smaller than similar DCNNs. We changed its architecture (the last softmax layer and the auxiliary classifier) and used transfer learning technology to retrain the common facility images in the metro tunnel. We use mean average precision (mAP) as the metric for performance evaluation. The results indicate that our recognition model achieved 90.81% mAP. Compared with the existing method, this method is a considerable improvement.

scholarly journals COVID-19 Face Mask Detection using Deep Convolutional Neural Networks & Computer Vision

2021 ◽  
Vol 14 (38) ◽  
pp. 2899-2915
Author(s):  
Premanand Ghadekar ◽  
◽  
Gurdeep Singh ◽  
Joydeep Datta ◽  
Aryan Kumar Gupta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Face Mask ◽  
Deep Convolutional Neural Networks

scholarly journals Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 258 ◽  
Author(s):  
Yu Feng ◽  
Frank Thiemann ◽  
Monika Sester
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Physical Reality ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cartographic Generalization

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

Application of convolutional neural networks for monitoring of marine objects

2020 ◽  
pp. 53-61
Author(s):  
Н.А. Полковникова ◽  
Е.В. Тузинкевич ◽  
А.Н. Попов
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Research Process ◽  
Probability Of Detection ◽  
Neural Network Architecture ◽  
Deep Convolutional Neural Networks

В статье рассмотрены технологии компьютерного зрения на основе глубоких свёрточных нейронных сетей. Применение нейронных сетей особенно эффективно для решения трудно формализуемых задач. Разработана архитектура свёрточной нейронной сети применительно к задаче распознавания и классификации морских объектов на изображениях. В ходе исследования выполнен ретроспективный анализ технологий компьютерного зрения и выявлен ряд проблем, связанных с применением нейронных сетей: «исчезающий» градиент, переобучение и вычислительная сложность. При разработке архитектуры нейросети предложено использовать функцию активации RELU, обучение некоторых случайно выбранных нейронов и нормализацию с целью упрощения архитектуры нейросети. Сравнение используемых в нейросети функций активации ReLU, LeakyReLU, Exponential ReLU и SOFTMAX выполнено в среде Matlab R2020a. На основе свёрточной нейронной сети разработана программа на языке программирования Visual C# в среде MS Visual Studio для распознавания морских объектов. Программапредназначена для автоматизированной идентификации морских объектов, производит детектирование (нахождение объектов на изображении) и распознавание объектов с высокой вероятностью обнаружения. The article considers computer vision technologies based on deep convolutional neural networks. Application of neural networks is particularly effective for solving difficult formalized problems. As a result convolutional neural network architecture to the problem of recognition and classification of marine objects on images is implemented. In the research process a retrospective analysis of computer vision technologies was performed and a number of problems associated with the use of neural networks were identified: vanishing gradient, overfitting and computational complexity. To solve these problems in neural network architecture development, it was proposed to use RELU activation function, training some randomly selected neurons and normalization for simplification of neural network architecture. Comparison of ReLU, LeakyReLU, Exponential ReLU, and SOFTMAX activation functions used in the neural network implemented in Matlab R2020a.The computer program based on convolutional neural network for marine objects recognition implemented in Visual C# programming language in MS Visual Studio integrated development environment. The program is designed for automated identification of marine objects, produces detection (i.e., presence of objects on image), and objects recognition with high probability of detection.

scholarly journals Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

2018 ◽  
Vol 10 (7) ◽  
pp. 1119 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Mohammad Rezaee ◽  
Fariba Mohammadimanesh ◽  
Yun Zhang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Support Vector Machine ◽  
State Of The Art ◽  
Support Vector ◽  
Learning Tools ◽  
Deep Convolutional Neural Networks ◽  
Multispectral Remote Sensing

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

scholarly journals Deep heterogeneous superpixel neural networks for image analysis and feature extraction

2021 ◽  
Author(s):  
◽  
Zhangwei (Alex) Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Image Analysis ◽  
Feature Extraction ◽  
Object Detection ◽  
Vision Research ◽  
Deep Convolutional Neural Networks ◽  
Efficient Representation ◽  
Weakly Supervised

Lately, deep convolutional neural networks are rapidly transforming and enhancing computer vision accuracy and performance, and pursuing higher-level and interpretable object recognition. Superpixel-based methodologies have been used in conventional computer vision research where their efficient representation has superior effects. In contemporary computer vision research driven by deep neural networks, superpixel-based approaches mainly rely on oversegmentation to provide a more efficient representation of the imagery data, especially when the computation is too expensive in time or memory to perform in pairwise similarity regularization or complex graphical probabilistic inference. In this dissertation, we proposed a novel superpixel-enabled deep neural network paradigm by relaxing some of the prior assumptions in the conventional superpixel-based methodologies and exploring its capabilities in the context of advanced deep convolutional neural networks. This produces novel neural network architectures that can achieve higher-level object relation modeling, weakly supervised segmentation, high explainability, and facilitate insightful visualizations. This approach has the advantage of being an efficient representation of the visual signal and has the capability to dissect out relevant object components from other background noise by spatially re-organizing visual features. Specifically, we have created superpixel models that join graphical neural network techniques and multiple-instance learning to achieve weakly supervised object detection and generate precise object bounding without pixel-level training labels. This dissection and the subsequent learning by the architecture promotes explainable models, whereby the human users of the models can see the parts of the objects that have led to recognition. Most importantly, this neural design's natural result goes beyond abstract rectangular bounds of an object occurrence (e.g., bounding box or image chip), but instead approaches efficient parts-based segmented recognition. It has been tested on commercial remote sensing satellite imagery and achieved success. Additionally, We have developed highly efficient monocular indoor depth estimation based on superpixel feature extraction. Furthermore, we have demonstrated state-of-theart weakly supervised object detection performance on two contemporary benchmark data sets, MS-COCO and VOC 2012. In the future, deep learning techniques based on superpixel-enabled image analysis can be further optimized in accuracy and computational performance; and it will also be interesting to evaluate in other research domains, such as those involving medical imagery, infrared imagery, or hyperspectral imagery.

Adaptive fault-tolerant automatic train operation using RBF neural networks

2014 ◽  
Vol 26 (1) ◽  
pp. 141-149 ◽  
Author(s):  
Shigen Gao ◽  
Hairong Dong ◽  
Bin Ning ◽  
Yao Chen ◽  
Xubin Sun
Keyword(s):  
Neural Networks ◽  
Fault Tolerant ◽  
Rbf Neural Networks ◽  
Train Operation ◽  
Automatic Train Operation

scholarly journals Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3368
Author(s):  
Mohammadreza Javadiha ◽  
Carlos Andujar ◽  
Enrique Lacasa ◽  
Angel Ric ◽  
Antonio Susin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Position Estimation ◽  
Estimation Methods ◽  
Large Set ◽  
High Angle ◽  
Deep Convolutional Neural Networks ◽  
Player Positions

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.

scholarly journals Kayu7net: Identifikasi dan Evaluasi F-Measure Citra Kayu berbasis Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 7 (6) ◽  
pp. 1089
Author(s):  
Iwan Muhammad Erwin ◽  
Risnandar Risnandar ◽  
Esa Prakarsa ◽  
Bambang Sugiarto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Iteration Process ◽  
Batch Size ◽  
Wood Identification ◽  
Deep Convolutional Neural Networks ◽  
Fully Connected ◽  
F Measure

<p class="Abstrak">Identifikasi kayu salah satu kebutuhan untuk mendukung pemerintah dan kalangan bisnis kayu untuk melakukan perdagangan kayu secara legal. Keahlian khusus dan waktu yang cukup dibutuhkan untuk memproses identifikasi kayu di laboratorium. Beberapa metodologi penelitian sebelumnya, proses identifikasi kayu masih dengan cara menggabungkan sistem manual menggunakan anatomi DNA kayu. Sedangkan penggunaan sistem komputer diperoleh dari citra penampamg melintang kayu secara proses mikrokopis dan makroskopis. Saat ini, telah berkembang teknologi computer vision dan machine learning untuk mengidentifikasi berbagai jenis objek, salah satunya citra kayu. Penelitian ini berkontribusi dalam mengklasifikasi beberapa spesies kayu yang diperdagangkan menggunakan Deep Convolutional Neural Networks (DCNN). Kebaruan penelitian ini terletak pada arsitektur DCNN yang bernama Kayu7Net. Arsitektur Kayu7Net yang diusulkan memiliki tiga lapisan konvolusi terhadap tujuh spesies dataset citra kayu. Pengujian dengan merubah citra input menjadi berukuran 600×600, 300×300, dan 128×128 piksel serta masing-masing diulang pada epoch 50 dan 100. DCNN yang diusulkan menggunakan fungsi aktivasi ReLU dengan batch size 32. ReLU bersifat lebih konvergen dan cepat saat proses iterasi. Sedangkan Fully-Connected (FC) berjumlah 4 lapisan akan menghasilkan proses training yang lebih efisien. Hasil eksperimen memperlihatkan bahwa Kayu7Net yang diusulkan memiliki nilai akurasi sebesar 95,54%, precision sebesar 95,99%, recall sebesar 95,54%, specificity sebesar 99,26% dan terakhir, nilai F-measure sebesar 95,46%. Hasil ini menunjukkan bahwa arsitektur Kayu7Net lebih unggul sebesar 1,49% pada akurasi, 2,49% pada precision, dan 5,26% pada specificity dibandingkan penelitian sebelumnya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>Wood identification is one of the needs to support the government and the wood business community for a legally wood trading system. Special expertise and sufficient time are needed to process wood identification in the laboratory. Some previous research works show that the process of identifying wood combines a manual system using a wood DNA anatomy. While, the use of a computer system is obtained from the wood image of microscopic and macroscopic process. Recently, the latest technology has developed by using the machine learning and computer vision to identify many objects, the one of them is wood image. This research contributes to classify several the traded wood species by using Deep Convolutional Neural Networks (DCNN). The novelty of this research is in the DCNN architecture, namely Kayu7Net. The proposed of Kayu7Net Architecture has three convolution layers of the seven species wood image dataset. The testing changes the wood image input to 600×600, 300×300, and 128×128 pixel, respectively, and each of them repeated until 50 and 100 epoches, respectively. The proposed DCNN uses the ReLU activation function and batch size 32. The ReLU is more convergent and faster during the iteration process. Whereas, the 4 layers of Fully-Connected (FC) will produce a more efficient training process. The experimental results show that the proposed Kayu7Net has an accuracy value of 95.54%, a precision of 95.99%, a recall of 95.54%, a specificity of 99.26% and finally, an F-measure value of 95.46%. These results indicate that Kayu7Net is superior by 1.49% of accuracy, 2.49% of precision, and 5.26% of specificity compared to the previous work. </em></p><p class="Abstrak"> </p>

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