scholarly journals DETECÇÃO DE ANIMAIS BOVINOS UTILIZANDO IMAGENS AÉREAS POR MEIO DE REDES NEURAIS

2021 ◽  
Vol 13 (2) ◽  
pp. 47-56
Author(s):  
Wellington Hiroshi Takano ◽  
Leandro Luiz de Almeida ◽  
Francisco Assis da Silva
Keyword(s):  
Neural Networks ◽  
Aerial Images ◽  
Physical Effort ◽  
Evolution Of Technology

Nowadays, with the evolution of technology, many areas that cover agriculture are making use of professional drones that have several tools that help in monitoring. With the advancement of Neural Networks, several researchers are choosing to use neural networks to detect objects. As the counting of bovine animals requires time and physical effort, in addition to being risky in certain situations, with the use of aerial images and neural networks, this activity becomes more viable and with less time spent. In this work, the focus is on detecting bovine animals using two neural networks, with aerial images captured from a drone.

scholarly journals Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Saleh Javadi ◽  
Mattias Dahl ◽  
Mats I. Pettersson
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Depth Maps

scholarly journals Implementación de un software para el análisis de imágenes aéreas multiespectrales de caña de azúcar [Implementation of software for the analysis of multispectral aerial images sugarcane]

2013 ◽  
Author(s):  
Gerardo Schneider ◽  
Alejandro Javier Hadad ◽  
Alejandra Kemerer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Crop Productivity ◽  
Software Implementation ◽  
Aerial Images ◽  
The Status

Resumen En este trabajo se presenta una implementación de software para la determinación del estado de plantaciones de caña de azúcar basado en el análisis de imágenes aéreas multiespectrales. En la actualidad no existen técnicas precisas para estimar objetivamente la superficie de caña caída o volcada, y esta ocasiona importantes pérdidas de productividad en la cosecha y en la industrialización. Para la realización de éste trabajo se confeccionó un dataset referencial de imágenes, y se implementó un software a partir del cual se obtuvieron indicadores propuestos como representativos del fenómeno agronómico, y se realizaron análisis de los datos generados. Además se implementó un software clasificador referencial basado en redes neuronales con el que se estimó la fortaleza de dichos indicadores y se estimó la superficie afectada en forma cuantitativa y espacial. Palabras ClavesCaña de azúcar, cuantificación, volcado, red neuronal, procesamiento de imagen   Abstract In this paper we present a software implementation for determining the status of sugarcane plantations based on the analysis of multispectral aerial images. Currently there are no precise techniques to estimate objectively the cane area fall or overturned, and this causes significant losses in crop productivity and industrialization. For the realization of this work a dataset benchmark images was made, and a software, from which were obtained representative proposed indicators for the agronomic phenomenon was implemented, and analyzes of the data generated were realized. In addition, we implemented a software benchmark classifier based on neural networks with which we estimated the strength of these indicators and the area affected was estimated quantitatively and spatially. Keywords Sugarcane, quantification, fall, neural network, image processing

Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

2018 ◽  
Vol 15 (2) ◽  
pp. 173-177 ◽  
Author(s):  
Kaiqiang Chen ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xin Gao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Aerial Images

LAND-USE CLASSIFICATION OF GRAY-SCALE AERIAL IMAGES USING PROBABILISTIC NEURAL NETWORKS

2004 ◽  
Vol 47 (5) ◽  
pp. 1813-1819 ◽  
Author(s):  
D. Ashish ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Aerial Images ◽  
Land Use Classification ◽  
Probabilistic Neural Networks ◽  
Gray Scale
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