Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery

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.