Vehicle Detection in Aerial Images Using Selective Search with a Simple Deep Learning Based Combination Classifier

2019 ◽  
pp. 221-233 ◽  
Author(s):  
Tanuja Tewari ◽  
Kaustubh V. Sakhare ◽  
Vibha Vyas
Keyword(s):  
Deep Learning ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Selective Search

scholarly journals Deep Learning for Vehicle Detection in Aerial Images

2018 ◽  
Author(s):  
Michael Ying Yang ◽  
Wentong Liao ◽  
Xinbo Li ◽  
Bodo Rosenhahn
Keyword(s):  
Deep Learning ◽  
Vehicle Detection ◽  
Aerial Images

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 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.

scholarly journals Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

2021 ◽  
Vol 273 ◽  
pp. 116490
Author(s):  
Odei Garcia-Garin ◽  
Toni Monleón-Getino ◽  
Pere López-Brosa ◽  
Asunción Borrell ◽  
Alex Aguilar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Automatic Detection ◽  
Aerial Images ◽  
Learning Approach ◽  
Detection And Quantification

scholarly journals IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Surface Model ◽  
Large Area ◽  
Digital Terrain ◽  
Terrain Models ◽  
Architectural Features ◽  
Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

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