A Study on Vehicle Detection through Aerial Images: Various Challenges, Issues and Applications

2021 ◽  
Author(s):  
Sandeep Kumar ◽  
E. G. Rajan ◽  
Shilpa Rani
Keyword(s):  
Vehicle Detection ◽  
Aerial Images

Robust Vehicle Detection in High-Resolution Aerial Images with Imbalanced Data

2021 ◽  
pp. 1-1
Author(s):  
Xianghui Li ◽  
Xinde Li ◽  
Zhijun Li ◽  
Xinran Xiong ◽  
Mohammad Omar Khyam ◽  
...  
Keyword(s):  
High Resolution ◽  
Imbalanced Data ◽  
Vehicle Detection ◽  
Aerial Images

Vehicle detection of parking lot with different resolution aerial images

2014 ◽  
Author(s):  
Zezhong Zheng ◽  
Yufeng Lu ◽  
Guoqing Zhou ◽  
Yalan Liu ◽  
Xiaowen Li ◽  
...  
Keyword(s):  
Vehicle Detection ◽  
Aerial Images ◽  
Parking Lot

scholarly journals A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250782
Author(s):  
Bin Wang ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Semantic Features ◽  
General Object ◽  
High Level ◽  
The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

Microscopic Traffic Data Collection by Remote Sensing

2003 ◽  
Vol 1855 (1) ◽  
pp. 121-128 ◽  
Author(s):  
S. P. Hoogendoorn ◽  
H. J. Van Zuylen ◽  
M. Schreuder ◽  
B. Gorte ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Digital Camera ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Lateral Position ◽  
Aerial Images ◽  
Detection And Tracking ◽  
Vehicle Trajectories ◽  
Vehicle Detection And Tracking ◽  
Traffic Data Collection

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

Sign in / Sign up

Export Citation Format

Share Document