Detecting objects in aerial images is challenging for at
least two reasons: (1) target objects like pedestrians are
very small in pixels, making them hardly distinguished from
surrounding background; and (2) targets are in general
sparsely and non-uniformly distributed, making the detection
very inefficient. In this paper, we address both issues
inspired by observing that these targets are often clustered.
In particular, we propose a Clustered Detection (ClusDet)
network that unifies object clustering and detection in an
end-to-end framework. The key components in ClusDet include
a cluster proposal sub-network (CPNet), a scale estimation
sub-network (ScaleNet), and a dedicated detection
network (DetecNet). Given an input image, CPNet produces
object cluster regions and ScaleNet estimates object scales
for these regions. Then, each scale-normalized cluster region
and their features are fed into DetecNet for object detection.
ClusDet has several advantages over previous solutions:
(1) it greatly reduces the number of chips for final
object detection and hence achieves high running time efficiency,
(2) the cluster-based scale estimation is more accurate
than previously used single-object based ones, hence
effectively improves the detection for small objects, and (3)
the final DetecNet is dedicated for clustered regions and implicitly
models the prior context information so as to boost
detection accuracy. The proposed method is tested on three
popular aerial image datasets including VisDrone, UAVDT
and DOTA. In all experiments, ClusDet achieves promising
performance in comparison with state-of-the-art detectors
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Density Map Guided Object Detection in Aerial Images
Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects. A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop. In this paper, we investigate the image cropping strategy to address these challenges. Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map. As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically. DMNet has three key components: a density map generation module, an image cropping module and an object detector. DMNet generates a density map and learns scale information based on density intensities to form cropping regions. Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, i.e. VisionDrone and UAVDT.
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- Award ID(s):
- 1910844
- NSF-PAR ID:
- 10193024
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition Workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
