Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tackling the Background Bias in Sparse Object Detection via Cropped
  Windows   Object detection on Unmanned Aerial Vehicles (UAVs) is still a challenging
task. The recordings are mostly sparse and contain only small objects. In this
work, we propose a simple tiling method that improves the detection capability
in the remote sensing case without modifying the model itself. By reducing the
background bias and enabling the usage of higher image resolutions during
training, our method can improve the performance of models substantially. The
procedure was validated on three different data sets and outperformed similar
approaches in performance and speed.

11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an