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Systematic evaluation of deep learning based detection frameworks for aerial imagery

: Sommer, L.; Steinmann, L.; Schumann, Arne; Beyerer, Jürgen


Sadjadi, F.A. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Automatic Target Recognition XXVIII : 16-17 April 2018, Orlando, Florida, United States
Bellingham, WA: SPIE, 2018 (Proceedings of SPIE 10648)
ISBN: 978-1-5106-1808-4
ISBN: 978-1-5106-1807-7
Paper 1064803, 13 pp.
Conference "Automatic Target Recognition" <28, 2018, Orlando/Fla.>
Conference "Defense and Security" <2018, Orlando/Fla.>
Conference "Defense and Commercial Sensing" (DCS) <2018, Orlando/Fla.>
Conference Paper
Fraunhofer IOSB ()
deep learning; vehicle detection; aerial imagery

Object detection in aerial imagery is crucial for many applications in the civil and military domain. In recent years, deep learning based object detection frameworks significantly outperformed conventional approaches based on hand-crafted features on several datasets. However, these detection frameworks are generally designed and optimized for common benchmark datasets, which considerably differ from aerial imagery especially in object sizes. As already demonstrated for Faster R-CNN, several adaptations are necessary to account for these differences. In this work, we adapt several state-of-the-art detection frameworks including Faster R-CNN, R-FCN, and Single Shot MultiBox Detector (SSD) to aerial imagery. We discuss adaptations that mainly improve the detection accuracy of all frameworks in detail. As the output of deeper convolutional layers comprise more semantic information, these layers are generally used in detection frameworks as feature map to locate and classify objects. However, the resolution of these feature maps is insufficient for handling small object instances, which results in an inaccurate localization or incorrect classification of small objects. Furthermore, state-of-the-art detection frameworks perform bounding box regression to predict the exact object location. Therefore, so called anchor or default boxes are used as reference. We demonstrate how an appropriate choice of anchor box sizes can considerably improve detection performance. Furthermore, we evaluate the impact of the performed adaptations on two publicly available datasets to account for various ground sampling distances or differing backgrounds. The presented adaptations can be used as guideline for further datasets or detection frameworks.