# Guided Attention Network for Object Detection and Counting on Drones

arXiv: https://arxiv.org/abs/1909.11307 · HTML: https://mlsanity.com/paper/1909.11307/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers](https://mlsanity.com/paper/2104.11896/) (2021)
- [Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)](https://mlsanity.com/paper/2108.05888/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark](https://mlsanity.com/paper/2108.07020/) (2021)
- [PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection](https://mlsanity.com/paper/2211.10763/) (2022)
- [Class Gradient Projection For Continual Learning](https://mlsanity.com/paper/2311.14905/) (2023)
- [Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning](https://mlsanity.com/paper/2102.06955/) (2021)
- [Counting and Locating High-Density Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2102.04366/) (2021)
- [Decoupled IoU Regression for Object Detection](https://mlsanity.com/paper/2202.00866/) (2022)
- [Dense Center-Direction Regression for Object Counting and Localization with Point Supervision](https://mlsanity.com/paper/2408.14457/) (2024)
- [Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach](https://mlsanity.com/paper/2607.04352/) (2026)
- [Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons](https://mlsanity.com/paper/2311.07955/) (2023)
- [Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images](https://mlsanity.com/paper/2505.23193/) (2025)
- [Alignment-Aware and Reliability-Gated Multimodal Fusion for Unmanned Aerial Vehicle Detection Across Heterogeneous Thermal-Visual Sensors](https://mlsanity.com/paper/2603.08208/) (2026)

## Abstract (arXiv)

Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new Guided Attention Network (GANet) to deal with both object detection and counting tasks based on the feature pyramid. Different from the previous methods relying on unsupervised attention modules, we fuse different scales of feature maps by using the proposed weakly-supervised Background Attention (BA) between the background and objects for more semantic feature representation. Then, the Foreground Attention (FA) module is developed to consider both global and local appearance of the object to facilitate accurate localization. Moreover, the new data argumentation strategy is designed to train a robust model in various complex scenes. Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and PUCPR+) show the state-of-the-art detection and counting performance of the proposed method compared with existing methods.