scholarly journals Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

2017 ◽  
Vol 9 (11) ◽  
pp. 1170 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Aerial Imagery

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

Vehicle detection in remote sensing images using denoizing-based convolutional neural networks

2016 ◽  
Vol 8 (3) ◽  
pp. 262-270 ◽  
Author(s):  
Hao Li ◽  
Kun Fu ◽  
Menglong Yan ◽  
Xian Sun ◽  
Hao Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Remote Sensing Images

scholarly journals Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2720 ◽  
Author(s):  
Jiandan Zhong ◽  
Tao Lei ◽  
Guangle Yao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Detection ◽  
Aerial Images

scholarly journals SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 687-692
Author(s):  
M. Madadikhaljan ◽  
R. Bahmanyar ◽  
S. M. Azimi ◽  
P. Reinartz ◽  
U. Sörgel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classical Model ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Haze Removal ◽  
The Neural Network ◽  
Image Dataset ◽  
Image Test

Abstract. Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image analysis systems, namely object detection and segmentation. Unlike rich haze removal literature in ground imagery, there is a lack of methods specifically designed for aerial imagery, considering the fact that there is a characteristic difference between the aerial imagery domain and ground one. In this paper, we propose a method to dehaze aerial images using Convolutional Neural Networks (CNNs). Currently, there is no available data for dehazing methods in aerial imagery. To address this issue, we have created a syntheticallyhazed aerial image dataset to train the neural network on aerial hazy image dataset. We train All-in-One dehazing network (AODNet) as the base approach on hazy aerial images and compare the performance of our proposed approach against the classical model. We have tested our model on natural as well as the synthetically-hazed aerial images. Both qualitative and quantitative results of the adapted network show an improvement in dehazing results. We show that the adapted AOD-Net on our aerial image test set increases PSNR and SSim by 2.2% and 9%, respectively.

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