scholarly journals Road Traffic Monitoring from UAV Images Using Deep Learning Networks

2021 ◽  
Vol 13 (20) ◽  
pp. 4027
Author(s):  
Sungwoo Byun ◽  
In-Kyoung Shin ◽  
Jucheol Moon ◽  
Jiyoung Kang ◽  
Sang-Il Choi
Keyword(s):  
Mean Squared Error ◽  
Road Traffic ◽  
Recall Rate ◽  
Traffic Monitoring ◽  
Learning Networks ◽  
Additional Information ◽  
Squared Error ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Precision Rate

In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV). The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating the speeds of vehicles on the roads. Our method can automatically measure the speed of the vehicles from the only videos recorded using UAV without additional information in both directions on the roads simultaneously. In our experiments, we evaluate the performance of the proposed method with the visual data at four different locations. The proposed method shows 97.6% recall rate and 94.7% precision rate in detecting vehicles, and it shows error (root mean squared error) of 5.27 km/h in estimating the speeds of vehicles.

scholarly journals Automated Bale Mapping Using Machine Learning and Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4675
Author(s):  
William Yamada ◽  
Wei Zhao ◽  
Matthew Digman
Keyword(s):  
Lower Cost ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Data Set ◽  
Map Projection ◽  
Squared Error ◽  
Aerial Vehicle ◽  
The Impact

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m.

scholarly journals Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

2021 ◽  
Vol 5 (4) ◽  
pp. 50
Author(s):  
Rafik Gouiaa ◽  
Moulay A. Akhloufi ◽  
Mozhdeh Shahbazi
Keyword(s):  
Neural Networks ◽  
Density Estimation ◽  
Traffic Monitoring ◽  
Crowd Counting ◽  
Convolution Neural Networks ◽  
Potential Applications ◽  
Comprehensive Survey ◽  
Camera Perspective ◽  
Aerial Vehicle ◽  
Uav Images

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images.

scholarly journals Semi-Supervised Minimum Error Entropy Principle with Distributed Method

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 968
Author(s):  
Baobin Wang ◽  
Ting Hu
Keyword(s):  
Gradient Descent ◽  
Mean Squared Error ◽  
Learning Ability ◽  
Minimum Error ◽  
Additional Information ◽  
Squared Error ◽  
Gradient Descent Algorithm ◽  
Entropy Principle ◽  
Non Gaussian ◽  
Distributed Method

The minimum error entropy principle (MEE) is an alternative of the classical least squares for its robustness to non-Gaussian noise. This paper studies the gradient descent algorithm for MEE with a semi-supervised approach and distributed method, and shows that using the additional information of unlabeled data can enhance the learning ability of the distributed MEE algorithm. Our result proves that the mean squared error of the distributed gradient descent MEE algorithm can be minimax optimal for regression if the number of local machines increases polynomially as the total datasize.

scholarly journals Forecasting PM10 in the Bay of Algeciras Based on Regression Models

2019 ◽  
Vol 11 (4) ◽  
pp. 968 ◽  
Author(s):  
José Palomares-Salas ◽  
Juan González-de-la-Rosa ◽  
Agustín Agüera-Pérez ◽  
José Sierra-Fernández ◽  
Olivia Florencias-Oliveros
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Average Concentration ◽  
Improve Performance ◽  
Urban Centers ◽  
Experimental Procedure ◽  
Additional Information ◽  
Squared Error ◽  
The Mean ◽  
Quality Indexes

Different forecasting methodologies, classified into parametric and nonparametric, were studied in order to predict the average concentration of P M 10 over the course of 24 h. The comparison of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient, the index of agreement, the mean absolute error, and the root mean squared error). The proposed experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras (Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained with the reference models through the introduction of low-quality measurements as exogenous information. This proves that it is possible to improve performance by using additional information from the existing nonlinear relationships between the concentration of the pollutants and the meteorological variables.

Road Traffic Monitoring

IEE Review ◽  
1989 ◽  
Vol 35 (5) ◽  
pp. 188
Author(s):  
P.L. Belcher
Keyword(s):  
Road Traffic ◽  
Traffic Monitoring

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

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