You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
By Adam Van Etten. arXiv.org, 2018
339 citations recorded by the source corpus.
Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the sheer number of pixels and geographic extent per image: a single DigitalGlobe satellite image encompasses >64 km2 and over 250 million pixels. Another challenge is that objects of interest are minuscule (often only ~10 pixels in extent), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (You Only Look Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of >0.5 km2/s. The proposed approach can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. We evaluate large test images at native resolution, and yield scores of F1 > 0.8 for vehicle localization. We further explore resolution and object size requirements by systematically testing the pipeline at decreasing resolution, and conclude that objects only ~5 pixels in size can still be localized with high confidence. Code is available at this https URL.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/a976f038aad172d1b0c6136d2a7436c73eba92a8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Computer Vision and Pattern Recognition
- Todd M. Bacastow
- David Lindenbaum
- Haowen Guo
- Ruixiang Zhang
- Adam Van Etten
- Jinwang Wang
- Wen Yang
- Guisong Xia
In Media Technology
- Todd M. Bacastow
- David Lindenbaum
- Haowen Guo
- Ruixiang Zhang
- Adam Van Etten
- Jinwang Wang
- Wen Yang
- Guisong Xia
Citations in the corpus
- Tiny Object Detection in Aerial Images (2021, background): “Unlike object detectors in natural scenes, most of the studies use anchor-based detectors to detect objects in aerial images, such as R-P-Faster R-CNN [35], YOLT [36], RoI Transformer [37], SCRDet [38],R2-CNN [4], and Mask OBB [2].”
- SpaceNet: A Remote Sensing Dataset and Challenge Series (2018, methodology): “We baselined performance with both YOLT (F1 = 0.60) and a modified version of MNC [27] (F1 = 0.57).”