scholarly journals ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding

2018 ◽  
Vol 4 (10) ◽  
pp. 116 ◽  
Author(s):  
Robail Yasrab
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Substantial Reduction ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Research Area ◽  
Smart Systems ◽  
Proposed Model ◽  
Driving Assistance ◽  
Flexible Architecture

This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer model, while the decoder network uses 16 upsampling and deconvolution units. The encoder of the network has a very flexible architecture that can be altered and trained for any size and resolution of images. The decoder network upsamples and maps the low-resolution encoder’s features. Consequently, there is a substantial reduction in the trainable parameters, as the network recycles the encoder’s pooling indices for pixel-wise classification and segmentation. The proposed model is intended to offer a simplified CNN model with less overhead and higher performance. The network is trained and tested on the famous road scenes dataset CamVid and offers outstanding outcomes in comparison to similar early approaches like FCN and VGG16 in terms of performance vs. trainable parameters.

Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1-20
Author(s):  
Kinjal V. Joshi ◽  
Narendra M. Patel
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Event Detection ◽  
Characteristic Curve ◽  
Research Area ◽  
Support Vector ◽  
Abnormal Event Detection ◽  
Proposed Model ◽  
Benchmark Datasets

Automatic abnormal event detection in a surveillance scene is very significant because of more consciousness about public safety. Because of usefulness and complexity, currently, it is an open research area. In this manuscript, the authors have proposed a novel convolutional neural network (CNN) model to detect an abnormal event in a surveillance scene. In this work, CNN is used in two ways. Firstly, it is used for both feature extraction and classification. In a second way, CNN is used for feature extraction, and support vector machine (SVM) is used for classification. Without any pre-processing, the proposed model gives better results compared to state-of-the-art methods. Experiments are carried out on four different publicly available benchmark datasets and one combined dataset, which contains all images of four datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). The experimental results determine the efficacy of the proposed model.

scholarly journals An Attention-Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shen Li ◽  
Hailong Zhang ◽  
Huachun Tan ◽  
Zhiyu Zhong ◽  
Zhuxi Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Research Area ◽  
Traffic Information ◽  
Driving Experience ◽  
The Road ◽  
Proposed Model ◽  
Battery Capacity

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input.

scholarly journals Automatic Detection of Pediatric Craniofacial Deformities using Convolutional Neural Networks

2020 ◽  
Vol 6 (3) ◽  
pp. 338-340
Author(s):  
Wattendorf Sonja ◽  
Tabatabaei Seyed Amir Hossein ◽  
Fischer Patrick ◽  
Hans-Peter Hans-Peter ◽  
Martina Wilbrand ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Application ◽  
Therapy Monitoring ◽  
Semantic Segmentation ◽  
Learning Technologies ◽  
Skull Shape ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Abnormal Head ◽  
Modern Machine

AbstractThe geometric shape of our skull is very important, not only from an esthetic perspective, but also from medical viewpoint. However, the lack of designated medical experts and wrong positioning is leading to an increasing number of abnormal head shapes in newborns and infants. To make screening and therapy monitoring for these abnormal shapes easier, we develop a mobile application to automatically detect and quantify such shapes. By making use of modern machine learning technologies like deep learning and transfer learning, we have developed a convolutional neural network for semantic segmentation of bird’s-eye view images of child heads. Using this approach, we have been able to achieve a segmentation accuracy of approximately 99 %, while having sensitivity and specificity of above 98 %. Given these promising results, we will use this basis to calculate medical parameters to quantify the skull shape. In addition, we will integrate the proposed model into a mobile application for further validation and usage in a real-world scenario.

scholarly journals Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1633 ◽  
Author(s):  
Beom-Su Kim ◽  
Sangdae Kim ◽  
Kyong Hoon Kim ◽  
Tae-Eung Sung ◽  
Babar Shah ◽  
...  
Keyword(s):  
Real Time ◽  
Routing Protocol ◽  
Traffic Management ◽  
Research Area ◽  
Multimedia Data ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Proposed Model ◽  
Event Information ◽  
Firm Model

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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