  Tiny Object Detection is challenging due to small size, low resolution,
occlusion, background clutter, lighting conditions and small object-to-image
ratio. Further, object detection methodologies often make underlying assumption
that both training and testing data remain congruent. However, this presumption
often leads to decline in performance when model is applied to
out-of-domain(unseen) data. Techniques like synthetic image generation are
employed to improve model performance by leveraging variations in input data.
Such an approach typically presumes access to 3D-rendered datasets. In
contrast, we propose a novel two-stage methodology Synthetic Randomized Image
Augmentation (SRIA), carefully devised to enhance generalization capabilities
of models encountering 2D datasets, particularly with lower resolution which is
more practical in real-world scenarios. The first stage employs a weakly
supervised technique to generate pixel-level segmentation masks. Subsequently,
the second stage generates a batch-wise synthesis of artificial images,
carefully designed with an array of diverse augmentations. The efficacy of
proposed technique is illustrated on challenging foreign object debris (FOD)
detection. We compare our results with several SOTA models including CenterNet,
SSD, YOLOv3, YOLOv4, YOLOv5, and Outer Vit on a publicly available FOD-A
dataset. We also construct an out-of-distribution test set encompassing 800
annotated images featuring a corpus of ten common categories. Notably, by
harnessing merely 1.81% of objects from source training data and amalgamating
with 29 runway background images, we generate 2227 synthetic images. Subsequent
model retraining via transfer learning, utilizing enriched dataset generated by
domain randomization, demonstrates significant improvement in detection
accuracy. We report that detection accuracy improved from an initial 41% to 92%
for OOD test set.
