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HWANet: A Haar Wavelet-based Attention Network for remote sensing object detection.

Baohua Jin¹, Fukang Yin¹, Wenpeng Cai¹

¹School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.

|September 4, 2025

Summary

This summary is machine-generated.

This study introduces HWANet, a novel Haar wavelet-based attention network for remote sensing object detection. It effectively handles scale variations, achieving high accuracy with fewer parameters.

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Area of Science:

Computer Vision
Machine Learning
Remote Sensing

Background:

Remote sensing object detection (RSOD) faces challenges with scale variations.
Current deep learning methods lose information during downsampling and lack context awareness.

Purpose of the Study:

To propose a novel network, HWANet, for improved RSOD.
To address information loss and enhance context modeling for multi-scale objects.

Main Methods:

Developed a Haar wavelet-based Attention Network (HWANet).
Introduced Low-frequency Enhanced Downsampling Module (LEM) to preserve object information.
Integrated Haar Frequency Domain Self-attention Module (HFDSA) and Spatial Information Interaction Module (SIIM) for context-aware multi-level feature integration.

Main Results:

HWANet achieved 93.1% mAP50 on NWPU VHR-10 and 99.1% mAP50 on SAR-Airport-1.0.
The model demonstrates superior performance with only 2.75M parameters.
Outperformed existing state-of-the-art methods in RSOD.

Conclusions:

HWANet effectively mitigates information loss during downsampling using Haar wavelets.
The network enhances context-aware modeling for superior multi-scale object detection.
HWANet offers a parameter-efficient and high-performing solution for RSOD.