Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vehicle Detection in Aerial Images   The detection of vehicles in aerial images is widely applied in many
applications. Comparing with object detection in the ground view images,
vehicle detection in aerial images remains a challenging problem because of
small vehicle size, monotone appearance and the complex background. In this
paper, we propose a novel double focal loss convolutional neural network
framework (DFL-CNN). In the proposed framework, the skip connection is used in
the CNN structure to enhance the feature learning. Also, the focal loss
function is used to substitute for conventional cross entropy loss function in
both of the region proposed network and the final classifier. We further
introduce the first large-scale vehicle detection dataset ITCVD with ground
truth annotations for all the vehicles in the scene. We demonstrate the
performance of our model on the existing benchmark DLR 3K dataset as well as
the ITCVD dataset. The experimental results show that our DFL-CNN outperforms
the baselines on vehicle detection.

8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph