Object Recognition in Aerial Images Using Convolutional Neural Networks
TLDR
Using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected, and classified from video feeds supplied by UAVs in real-time.Abstract:
There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.read more
Citations
More filters
Journal ArticleDOI
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Omid Ghorbanzadeh,Thomas Blaschke,Khalil Gholamnia,Sansar Raj Meena,Dirk Tiede,Jagannath Aryal +5 more
TL;DR: The CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner, Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the results of augmentation strategies to artificially increase the number of existing samples are better understanding.
Journal ArticleDOI
Drone-surveillance for search and rescue in natural disaster
TL;DR: An image dataset for human action detection for SAR inspired by the pyramidal feature extraction of SSD for human detection and action recognition is proposed and proposed model achieves 7% higher mAP value when applied to standard Okutama dataset in comparison with the state-of-the-art detection models in literature.
Journal ArticleDOI
Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
TL;DR: In this article, a machine learning algorithm (MLA) and a deep learning algorithm(DLA) were used to develop groundwater potential maps using support vector regression (SVR) and convolution neural network (CNN) functions, respectively.
Journal ArticleDOI
Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations
Eleftherios Lygouras,Nicholas Santavas,Anastasios Taitzoglou,K.N. Tarchanidis,Athanasios C. Mitropoulos,Antonios Gasteratos +5 more
TL;DR: Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers and allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities.
Journal ArticleDOI
Convolutional neural networks for object detection in aerial imagery for disaster response and recovery
TL;DR: This research introduces and evaluates a series of convolutional neural network (CNN) models for ground object detection from aerial views of disaster’s aftermath that are capable of recognizing critical ground assets including building roofs (both damaged and undamaged), vehicles, vegetation, debris, and flooded areas.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
TL;DR: This work equips the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
Book ChapterDOI
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
TL;DR: This work equips the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
Posted Content
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TL;DR: DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.