  Recently, many researchers have attempted to improve deep learning-based
object detection models, both in terms of accuracy and operational speeds.
However, frequently, there is a trade-off between speed and accuracy of such
models, which encumbers their use in practical applications such as autonomous
navigation. In this paper, we explore a meta-cognitive learning strategy for
object detection to improve generalization ability while at the same time
maintaining detection speed. The meta-cognitive method selectively samples the
object instances in the training dataset to reduce overfitting. We use YOLO v3
Tiny as a base model for the work and evaluate the performance using the MS
COCO dataset. The experimental results indicate an improvement in absolute
precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference
time.
