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

arXiv: https://arxiv.org/abs/1903.01347 · HTML: https://mlsanity.com/paper/1903.01347/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [Oriented Bounding Boxes for Small and Freely Rotated Objects](https://mlsanity.com/paper/2104.11854/) (2021)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance](https://mlsanity.com/paper/2104.03888/) (2021)
- [Empirical Evaluation of Physical Adversarial Patch Attacks Against Overhead Object Detection Models](https://mlsanity.com/paper/2206.12725/) (2022)
- [Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning](https://mlsanity.com/paper/2206.06510/) (2022)
- [Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery](https://mlsanity.com/paper/2507.11040/) (2025)
- [Towards Packaging Unit Detection for Automated Palletizing Tasks](https://mlsanity.com/paper/2308.06306/) (2023)

## Abstract (arXiv)

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.