scholarly journals Deep Neural Networks Approach for Monitoring Vehicles on the Highway

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 163
Author(s):  
Amir Mahmud Husein ◽  
Christopher Christopher ◽  
Andy Gracia ◽  
Rio Brandlee ◽  
Muhammad Haris Hasibuan
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Mean Average Precision ◽  
Vehicle Classification ◽  
Average Precision ◽  
Video File ◽  
Smart Transportation ◽  
One Stage ◽  
Vehicle Information

Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.

Outperforming Dermatologist-Level Skin Cancer Classification via Enhanced Training of Deep Neural Networks (Preprint)

2018 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Christof von Kalle
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Deep Neural Networks ◽  
Challenge Test ◽  
Image Data ◽  
Cancer Classification ◽  
Receiver Operating Curve ◽  
Training Procedure ◽  
Average Precision ◽  
Test Sets

BACKGROUND In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. These CNNs failed to outperform the International Symposium on Biomedical Imaging (ISBI) 2016 challenge in terms of average precision, however, so the technical progress represented by these studies is limited. In addition, the available reports are difficult to reproduce, due to incomplete descriptions of training procedures and the use of proprietary image databases. These factors prevent the comparison of various CNN classifiers in equal terms. OBJECTIVE To demonstrate the training of an image-classifier CNN that outperforms the winner of the ISBI 2016 challenge by using open source images exclusively. METHODS A detailed description of the training procedure is reported while the used images and test sets are disclosed fully, to insure the reproducibility of our work. RESULTS Our CNN classifier outperforms all recent attempts to classify the original ISBI 2016 challenge test data (full set of 379 test images), with an average precision of 0.709 (vs. 0.637 of the ISBI winner) and with an area under the receiver operating curve of 0.85. CONCLUSIONS This work illustrates the potential for improving skin cancer classification with enhanced training procedures for CNNs, while avoiding the use of costly equipment or proprietary image data.

Improving Vehicle Classification and Detection with Deep Neural Networks

2020 ◽  
Author(s):  
Ahmed K. Mohamed ◽  
Ahmed K. Ibrahim ◽  
Ahmad Akram ◽  
Abdullah Ibrahim ◽  
Mohamed Gamal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Vehicle Classification

scholarly journals Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways

2020 ◽  
Author(s):  
Mateus Eloi da Silva Bastos ◽  
Vitor Yeso Fidelis Freitas ◽  
Richardson Santiago Teles De Menezes ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Distance Estimation ◽  
Mean Average Precision ◽  
Aerial Images ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Training Set ◽  
Average Precision ◽  
Safety Distance

In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.

scholarly journals Mice Tracking Using The YOLO Algorithm

2020 ◽  
Author(s):  
Richardson Santiago Teles De Menezes ◽  
John Victor Alves Luiz ◽  
Aron Miranda Henrique-Alves ◽  
Rossana Moreno Santa Cruz ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Behavioral Neuroscience ◽  
High Accuracy ◽  
Mean Average Precision ◽  
Computational Tool ◽  
Training Set ◽  
Average Precision ◽  
The Mean

The computational tool developed in this study is based on convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation, and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way.

Computer vision and deep learning–based data anomaly detection method for structural health monitoring

2018 ◽  
Vol 18 (2) ◽  
pp. 401-421 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li ◽  
Yufeng Zhang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Monitoring Systems ◽  
Structural Health ◽  
Health Monitoring Systems ◽  
Incorrect Data

The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.

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