Object Detection in Aerial Images with Uncertainty-Aware Graph Network
- URL: http://arxiv.org/abs/2208.10781v2
- Date: Wed, 24 Aug 2022 05:45:37 GMT
- Title: Object Detection in Aerial Images with Uncertainty-Aware Graph Network
- Authors: Jongha Kim, Jinheon Baek, Sung Ju Hwang
- Abstract summary: We propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects.
We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet)
- Score: 61.02591506040606
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we propose a novel uncertainty-aware object detection framework
with a structured-graph, where nodes and edges are denoted by objects and their
spatial-semantic similarities, respectively. Specifically, we aim to consider
relationships among objects for effectively contextualizing them. To achieve
this, we first detect objects and then measure their semantic and spatial
distances to construct an object graph, which is then represented by a graph
neural network (GNN) for refining visual CNN features for objects. However,
refining CNN features and detection results of every object are inefficient and
may not be necessary, as that include correct predictions with low
uncertainties. Therefore, we propose to handle uncertain objects by not only
transferring the representation from certain objects (sources) to uncertain
objects (targets) over the directed graph, but also improving CNN features only
on objects regarded as uncertain with their representational outputs from the
GNN. Furthermore, we calculate a training loss by giving larger weights on
uncertain objects, to concentrate on improving uncertain object predictions
while maintaining high performances on certain objects. We refer to our model
as Uncertainty-Aware Graph network for object DETection (UAGDet). We then
experimentally validate ours on the challenging large-scale aerial image
dataset, namely DOTA, that consists of lots of objects with small to large
sizes in an image, on which ours improves the performance of the existing
object detection network.
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