pith. sign in

arxiv: 1903.01347 · v2 · pith:OWARNVK4new · 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
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  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.