Book ChapterDOI
Real-Time Vehicle Detection in Aerial Images Using Skip-Connected Convolution Network with Region Proposal Networks
Somsukla Maiti,Prashant Gidde,Sumeet Saurav,Sanjay Singh,Dhiraj,Santanu Chaudhury +5 more
- pp 200-208
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TLDR
This paper aims to provide a solution to the problem faced in real-time vehicle detection in aerial images and videos by using hyper maps generated by skip connected Convolutional network to generate object like proposals accurately.Abstract:
Detection of objects in aerial images has gained significant attention in recent years, due to its extensive needs in civilian and military reconnaissance and surveillance applications. With the advent of Unmanned Aerial Vehicles (UAV), the scope of performing such surveillance task has increased. The small size of the objects in aerial images makes it very difficult to detect them. Two-stage Region based Convolutional Neural Network framework for object detection has been proved quite effective. The main problem with these frameworks is the low speed as compared to the one class object detectors due to the computation complexity in generating the region proposals. Region-based methods suffer from poor localization of the objects that leads to a significant number of false positives. This paper aims to provide a solution to the problem faced in real-time vehicle detection in aerial images and videos. The proposed approach used hyper maps generated by skip connected Convolutional network. The hyper feature maps are then passed through region proposal network to generate object like proposals accurately. The issue of detecting objects similar to background is addressed by modifying the loss function of the proposal network. The performance of the proposed network has been evaluated on the publicly available VEDAI dataset.read more
Citations
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Journal ArticleDOI
Vehicle Detection From UAV Imagery With Deep Learning: A Review.
TL;DR: In this article , the authors provide a review on vehicle detection from UAV imagery using deep learning techniques, including convolutional neural networks, recurrent neural network, autoencoders, generative adversarial networks, and their contribution to improve the vehicle detection task.
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Inferring Visual Biases in UAV Videos from Eye Movements
TL;DR: This work designed a process to extract new visual attention biases in the UAV imagery, leading to the definition of a new dictionary of visual biases, and conducts a benchmark on two different datasets, whose results confirm that the 20 defined biases are relevant as a low-complexity saliency prediction system.
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Using single and multiple unmanned aerial vehicles for microscopic driver behaviour data collection at freeway interchange ramps
TL;DR: In this paper , a detailed methodological framework for collecting microscopic driver and vehicle behaviour data over a long road segment with an application to the entire stretch of a freeway ramp segment using single and multiple unmanned aerial vehicles (UAVs).
References
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Journal ArticleDOI
Vehicle detection in aerial imagery
TL;DR: A new database of aerial images provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments and gives the performance of baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.
Proceedings ArticleDOI
Orientation robust object detection in aerial images using deep convolutional neural network
TL;DR: This paper proposes to use Deep Convolutional Neural Network features from combined layers to perform orientation robust aerial object detection, and explores the inherent characteristics of DC-NN as well as relate the extracted features to the principle of disentangling feature learning.
Journal ArticleDOI
Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.
TL;DR: An improved detection method based on Faster R-CNN is proposed, which employs a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps and replaces the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions.
Journal ArticleDOI
Convolutional Neural Network Based Automatic Object Detection on Aerial Images
Igor Sevo,Aleksej Avramovic +1 more
TL;DR: This letter presents an automatic content-based analysis of aerial imagery in order to detect and mark arbitrary objects or regions in high-resolution images and proposes a method for automatic object detection based on a convolutional neural network.
Journal ArticleDOI
Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks
TL;DR: To accurately extract vehicle-like targets, an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection is developed and a coupled R-CNN method is proposed, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously.