Title
Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery.
Abstract
Vehicle detection in remote sensing imagery is a prominent issue over the last few years. In this application, the processing of optical remote sensing images becomes critical due to the complex environment, large size, occlusions and color variations. However, several approaches have been proposed to improve the training process but still, the efforts are moving towards optimal solutions. The training process is time-consuming and a large amount of memory is required to store those training images. Numerous traditional state-of-the-art approaches are suffering from problematic high computational time. In this paper, a new training methodology which is based on compressed and down-scaled images is implemented to reduce the training time. The training images are compressed at Quality Factor (QF) of 50 and down-scaled by scale factor of 0.5 to evaluate the performance for vehicle detection. The existing approaches of computer vision are taking advantage of high computational Graphical Processing Units (GPUs) to speed up the training process. The proposed framework is also a better way to reduce the computational time. To compare performance, we have trained the RCNN, Fast-RCNN, Faster-RCNN and Cascade detectors by using three types of training image sets and several experiments have been performed. More specifically, our approach makes the training faster than the training based on original images and training based on compressed images provides optimal results.
Year
DOI
Venue
2019
10.1007/s11042-019-08033-x
Multimedia Tools and Applications
Keywords
Field
DocType
Vehicle detection, Machine learning, Compressed, Down-scaled, Regions, RCNN
Scale factor,Computer vision,Computer science,Remote sensing,Vehicle detection,Artificial intelligence,Cascade,Detector,Speedup
Journal
Volume
Issue
ISSN
78
22
1380-7501
Citations 
PageRank 
References 
1
0.37
0
Authors
5
Name
Order
Citations
PageRank
Shahid Karim143.42
Ye Zhang230443.70
Shoulin Yin365.87
Asif Ali4146.77
Ali Anwar Brohi510.37