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Accurate UAV Small Object Detection Based on HRFPN and EfficentVMamba.

Shixiao Wu¹, Xingyuan Lu², Chengcheng Guo^3,4

¹School of Information Engineering, Wuhan Business University, Wuhan 430056, China.

Sensors (Basel, Switzerland)

|August 10, 2024

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This study introduces HRMamba-YOLO, an advanced algorithm for detecting small objects in drone imagery. The novel approach significantly improves detection accuracy, outperforming existing methods on multiple datasets.

Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Small objects in Unmanned Aerial Vehicle (UAV) images present detection challenges due to scattering, occlusion, noise, and limited features.
Existing methods struggle with the scarcity of effective features for small object detection in UAV imagery.

Purpose of the Study:

To develop a novel algorithm, High-Resolution Feature Pyramid Network Mamba-Based YOLO (HRMamba-YOLO), for enhanced small object detection in UAV imagery.
To improve feature extraction and contextual information capture for more robust small object identification.

Main Methods:

The HRMamba-YOLO algorithm integrates High-Resolution Network (HRNet), EfficientVMamba, and YOLOv8.
Key modules include Double Spatial Pyramid Pooling (Double SPP), Efficient Mamba Module (EMM), and Fusion Mamba Module (FMM).

Keywords:

HRNet Mamba YOLO deep learning feature fusion small object detection

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A High-Resolution Feature Pyramid Network (HRFPN) and FMM enhance feature interactions and fusion for improved detection.

Main Results:

HRMamba-YOLO achieved a 4.4% higher Mean Average Precision (mAP) on the VisDroneDET dataset compared to YOLOv8-m.
On the Dota1.5 dataset, the algorithm reached 37.1% mAP, outperforming YOLOv8-m by 3.8%.
Improvements of 1.5% and 0.3% mAP were observed on the UCAS_AOD and DIOR datasets, respectively, without pre-trained models.

Conclusions:

HRMamba-YOLO demonstrates superior performance and efficiency in detecting small objects within UAV images.
The study offers innovative solutions and valuable insights for future research in small object detection.