  Computing at the edge offers intriguing possibilities for the development of
autonomy and artificial intelligence. The advancements in autonomous
technologies and the resurgence of computer vision have led to a rise in demand
for fast and reliable deep learning applications. In recent years, the industry
has introduced devices with impressive processing power to perform various
object detection tasks. However, with real-time detection, devices are
constrained in memory, computational capacity, and power, which may compromise
the overall performance. This could be solved either by optimizing the object
detector or modifying the images. In this paper, we investigate the performance
of CNN-based object detectors on constrained devices when applying different
image compression techniques. We examine the capabilities of a NVIDIA Jetson
Nano; a low-power, high-performance computer, with an integrated GPU, small
enough to fit on-board a CubeSat. We take a closer look at the Single Shot
MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN)
that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in
Aerial Images. The performance is measured in terms of inference time, memory
consumption, and accuracy. By applying image compression techniques, we are
able to optimize performance. The two techniques applied, lossless compression
and image scaling, improves speed and memory consumption with no or little
change in accuracy. The image scaling technique achieves a 100% runnable
dataset and we suggest combining both techniques in order to optimize the
speed/memory/accuracy trade-off.
