# Vehicle Detection in Aerial Images

arXiv: https://arxiv.org/abs/1801.07339 · HTML: https://mlsanity.com/paper/1801.07339/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

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

- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [Source-free Domain Adaptive Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2401.17916/) (2024)
- [Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset](https://mlsanity.com/paper/2111.12122/) (2021)
- [Deep Vehicle Detection in Satellite Video](https://mlsanity.com/paper/2204.06828/) (2022)
- [Vehicle Detection Performance in Nordic Region](https://mlsanity.com/paper/2403.15017/) (2024)
- [A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery](https://mlsanity.com/paper/2204.02325/) (2022)
- [Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning](https://mlsanity.com/paper/2401.00986/) (2024)

## Abstract (arXiv)

The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size, monotone appearance and the complex background. In this paper, we propose a novel double focal loss convolutional neural network framework (DFL-CNN). In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark DLR 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.