Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples

2019 ◽  
Vol 28 (8) ◽  
pp. 4117-4129 ◽  
Author(s):  
Sorour Mohajerani ◽  
Parvaneh Saeedi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shadow Detection ◽  
Adversarial Examples ◽  
Rgb Images

scholarly journals Performance Analysis in the Segmentation of urban asphalted roads in RGB satellite images using K-Means++ and SegNet

2021 ◽  
Vol 24 (68) ◽  
pp. 89-103
Author(s):  
João Batista Pacheco Junior ◽  
Henrique Mariano Costa do Amaral
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Real Life ◽  
The Other ◽  
Computational Tool ◽  
Average Accuracy ◽  
Proposal A ◽  
Rgb Images ◽  
Associated Data

The design and manual insertion of new terrestrial roads into geographic databases is a frequent activity in geoprocessing and their demand usually occurs as the most up-to-date satellite imagery of the territory is acquired. Continually, new urban and rural occupations emerge, for which specific vector geometries need to be designed to characterize the cartographic inputs and accommodate the relevant associated data. Therefore, it is convenient to develop a computational tool that, with the help of artificial intelligence, automates what is possible in this respect, since manual editing depends on the limits of user agility, and does it in images that are usually easy and free to access. To test the feasibility of this proposal, a database of RGB images containing asphalted urban roads is presented to the K-Means++ algorithm and the SegNet Convolutional Neural Network, and the performance of each was evaluated and compared for accuracy and IoU of road identification. Under the conditions of the experiment, K-Means++ achieved poor and unviable results for use in a real-life application involving tarmac detection in RGB satellite images, with average accuracy ranging from 41.67% to 64.19% and average IoU of 12.30% to 16.16%, depending on the preprocessing strategy used. On the other hand, the SegNet Convolutional Neural Network proved to be appropriate for precision applications not sensitive to discontinuities, achieving an average accuracy of 87.12% and an average IoU of 71.93%.

scholarly journals Monkey Features Location Identification Using Convolutional Neural Networks

2018 ◽  
Author(s):  
Rollyn Labuguen (P) ◽  
Vishal Gaurav ◽  
Salvador Negrete Blanco ◽  
Jumpei Matsumoto ◽  
Kenichi Inoue ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Animal Behavior ◽  
Natural Environment ◽  
Feature Detection ◽  
Natural Habitat ◽  
Deep Convolutional Neural Network ◽  
Rgb Images

AbstractUnderstanding animal behavior in its natural habitat is a challenging task. One of the primary step for analyzing animal behavior is feature detection. In this study, we propose the use of deep convolutional neural network (CNN) to locate monkey features from raw RGB images of monkey in its natural environment. We train the model to identify features such as the nose and shoulders of the monkey at about 0.01 model loss.

scholarly journals Mapping Single Palm-Trees Species in Forest Environments with a Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Luciene Sales Dagher Arce ◽  
Mauro dos Santos de Arruda ◽  
Danielle Elis Garcia Furuya ◽  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
State Of The Art ◽  
Palm Tree ◽  
Forest Environment ◽  
Individual Tree ◽  
Palm Trees ◽  
Rgb Images

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

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