Name
Title | ||
|---|---|---|
Multi Feature Deconvolutional Faster R-CNN for Precise Vehicle Detection in Aerial Imagery |
Abstract | ||
|---|---|---|
Accurate detection of objects in aerial images is an important task for many applications such as traffic monitoring, surveillance, reconnaissance and rescue tasks. Recently, deep learning based detection frameworks clearly improved the detection performance on aerial images compared to conventional methods comprised of hand-crafted features and a classifier within a sliding window approach. These deep learning based detection frameworks use the output of the last convolutional layer as feature map for localization and classification. Due to the small size of objects in aerial images, only shallow layers of standard models like VGG-16 or small networks are applicable in order to provide a sufficiently high feature map resolution. However, high-resolution feature maps offer less semantic and contextual information, which results in approaches being more prone to false alarms due to objects with similar shapes especially in case of tiny objects. In this paper, we extend the Faster R-CNN detection framework to cope this issue. Therefore, we apply a deconvolutional module that up-samples low-dimensional feature maps of deep layers and combines the up-sampled features with the features of shallow layers while the feature map resolution is kept sufficiently high to localize tiny objects. Our proposed deconvolutional framework clearly outperforms state-of-the-art methods on two publicly available datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WACV.2018.00075 | 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | Field | DocType |
tiny objects,Faster R-CNN detection framework,low-dimensional feature maps,deep layers,shallow layers,deconvolutional framework,multifeature deconvolutional Faster R-CNN,precise vehicle detection,aerial imagery,traffic monitoring,reconnaissance,rescue tasks,deep learning,detection performance,sliding window approach,convolutional layer,high-resolution feature maps | Computer vision,Sliding window protocol,Similarity (geometry),Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Deep learning,Classifier (linguistics),Image resolution,Detector,Semantics | Conference |
ISSN | ISBN | Citations |
2472-6737 | 978-1-5386-4887-2 | 1 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lars Wilko Sommer | 1 | 31 | 9.49 |
| Arne Schumann | 2 | 85 | 14.01 |
| Tobias Schuchert | 3 | 93 | 12.21 |
| Jürgen Beyerer | 4 | 315 | 75.37 |