  We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable
approach for small object detection, anchored in our inference-data-centric
philosophy. Dynamic Tiling starts with non-overlapping tiles for initial
detections and utilizes dynamic overlapping rates along with a tile minimizer.
This dual approach effectively resolves fragmented objects, improves detection
accuracy, and minimizes computational overhead by reducing the number of
forward passes through the object detection model. Adaptable to a variety of
operational environments, our method negates the need for laborious
recalibration. Additionally, our large-small filtering mechanism boosts the
detection quality across a range of object sizes. Overall, Dynamic Tiling
outperforms existing model-agnostic uniform cropping methods, setting new
benchmarks for efficiency and accuracy.

Computer Vision and Pattern Recognition;Artificial Intelligence