# Object detection on aerial imagery using CenterNet

arXiv: https://arxiv.org/abs/1908.08244 · HTML: https://mlsanity.com/paper/1908.08244/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Tackling the Background Bias in Sparse Object Detection via Cropped Windows](https://mlsanity.com/paper/2106.02288/) (2021)
- [UAV-based Crowd Surveillance in Post COVID-19 Era](https://mlsanity.com/paper/2111.14176/) (2021)
- [Comprehensive Analysis of the Object Detection Pipeline on UAVs](https://mlsanity.com/paper/2203.00306/) (2022)

## Abstract (arXiv)

Detection and classification of objects in aerial imagery have several applications like urban planning, crop surveillance, and traffic surveillance. However, due to the lower resolution of the objects and the effect of noise in aerial images, extracting distinguishing features for the objects is a challenge. We evaluate CenterNet, a state of the art method for real-time 2D object detection, on the VisDrone2019 dataset. We evaluate the performance of the model with different backbone networks in conjunction with varying resolutions during training and testing.