| Summary: | The rapid advancement of object detection models in recent years has provided fast
and accurate object detections on critical applications such as autonomous driving
and healthcare services. While current-state-of-the art object detection models have
achieved remarkable accuracy, it is at the expense of high computational cost and a
significant amount of training time and data. In this project, we investigate various
compressive sensing techniques, such as applying SVD compression and SVD
background subtraction on the training images with the YOLOv5 object detection
model in an attempt to investigate the benefit of compressive sensing on object
detection tasks. The SVD-IC dataset, with SVD compression applied on the training
dataset, outperformed the original dataset by achieving 7.9% higher mAP and 3.1%
higher precision accuracy during testing. When SVD compression and SVD
background subtraction were applied to the training dataset, it was observed that it
enhances performance during the early stage of training. Systems with lower
computational resources are able to benefit from SVD compression, where
compressed training images would result in lower storage usage, as well as obtaining
object detection models with better performance when trained with low number of
epochs.
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