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arxiv 1903.01347 v2 pith:OWARNVK4 submitted 2019-03-04 cs.CV

Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery

Nikolay Sergievskiy , Alexander Ponamarev This is my paper
classification cs.CV
keywords challengedatasetdetectionxviewdiuxfocalimageryimbalanced
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper describes our approach to the DIUx xView 2018 Detection Challenge [1]. This challenge focuses on a new satellite imagery dataset. The dataset contains 60 object classes that are highly imbalanced. Due to the imbalanced nature of the dataset, the training process becomes significantly more challenging. To address this problem, we introduce a novel Reduced Focal Loss function, which brought us 1st place in the DIUx xView 2018 Detection Challenge.

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Cited by 1 Pith paper

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  1. A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data

    cs.CV 2019-07 accept novelty 2.0

    Nearest-neighbor interpolation matches multi-scale deep super-resolution performance for vehicle detection on 4x-upscaled xView satellite imagery, with a 0.0002 AP difference.