Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks

2021 ◽  
pp. 254-267
Author(s):  
Stefan Hensel ◽  
Marin B. Marinov ◽  
Max Schmitt
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Convolutional Neural Networks ◽  
Aerial Vehicles

scholarly journals Classification of UAVs' distorted images using Convolutional Neural Networks

2020 ◽  
Author(s):  
Leandro Silva ◽  
Jocival D. Júnior ◽  
Jean Santos ◽  
João Fernando Mari ◽  
Maurício Escarpinati ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Aerial Vehicles ◽  
Promising Area

Currently, the use of unmanned aerial vehicles (UAVs) is becoming ever more common for acquiring images in precision agriculture, either to identify characteristics of interest or to estimate plantations. However, despite this growth, their processing usually requires specialized techniques and software. During flight, UAVs may undergo some variations, such as wind interference and small altitude variations, which directly influence the captured images. In order to address this problem, we proposed a Convolutional Neural Network (CNN) architecture for the classification of three linear distortions common in UAV flight: rotation, translation and perspective transformations. To train and test our CNN, we used two mosaics that were divided into smaller individual images and then artificially distorted. Results demonstrate the potential of CNNs for solving possible distortions caused in the images during UAV flight. Therefore this becomes a promising area of exploration.

Real-Time Puddle Detection Using Convolutional Neural Networks with Unmanned Aerial Vehicles

2021 ◽  
Author(s):  
Mehmet Bilge Han Tas ◽  
Muhammed Coskun Irmak ◽  
Sedat Turan ◽  
Abdulsamet Hasiloglu
Keyword(s):  
Neural Networks ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Aerial Vehicles

scholarly journals Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

2021 ◽  
Vol 5 (2 (113)) ◽  
pp. 6-21
Author(s):  
Vadym Slyusar ◽  
Mykhailo Protsenko ◽  
Anton Chernukha ◽  
Pavlo Kovalov ◽  
Pavlo Borodych ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Unmanned Aerial Vehicles ◽  
Convolutional Neural Networks ◽  
Aerial Photographs ◽  
Problem Solution ◽  
Deep Convolutional Neural Networks ◽  
Test Set ◽  
Aerial Vehicles ◽  
Proposed Model

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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