# Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection

arXiv: https://arxiv.org/abs/2311.12956 · HTML: https://mlsanity.com/paper/2311.12956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://mlsanity.com/paper/1703.01086/) (2017)
- [Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://mlsanity.com/paper/2203.06717/) (2022)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity](https://mlsanity.com/paper/2207.03620/) (2022)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://mlsanity.com/paper/1608.07916/) (2016)
- [Large Selective Kernel Network for Remote Sensing Object Detection](https://mlsanity.com/paper/2303.09030/) (2023)
- [Learning to Divide and Conquer for Online Multi-Target Tracking](https://mlsanity.com/paper/1509.03956/) (2015)

## Built on by (most cited first, 1 of 1)

- [RGBX-DiffusionDet: A Framework for Multi-Modal RGB-X Object Detection Using DiffusionDet](https://mlsanity.com/paper/2505.02586/) (2025)

## Abstract (arXiv)

In the realm of aerial image analysis, object detection plays a pivotal role, with significant implications for areas such as remote sensing, urban planning, and disaster management. This study addresses the inherent challenges in this domain, notably the detection of small objects, managing densely packed elements, and accounting for diverse orientations. We present an in-depth evaluation of an object detection model that integrates the Large Selective Kernel Network (LSKNet)as its backbone with the DiffusionDet head, utilizing the iSAID dataset for empirical analysis. Our approach encompasses the introduction of novel methodologies and extensive ablation studies. These studies critically assess various aspects such as loss functions, box regression techniques, and classification strategies to refine the model's precision in object detection. The paper details the experimental application of the LSKNet backbone in synergy with the DiffusionDet heads, a combination tailored to meet the specific challenges in aerial image object detection. The findings of this research indicate a substantial enhancement in the model's performance, especially in the accuracy-time tradeoff. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement, outperforming the RCNN model by 4.7% on the same dataset. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis, paving the way for more accurate and efficient object detection methodologies. The code is publicly available at https://github.com/SashaMatsun/LSKDiffDet