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Related Experiment Video

Updated: Jun 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

A Lightweight Remote Sensing Small Target Image Detection Algorithm Based on Improved YOLOv8.

Haijiao Nie¹, Huanli Pang¹, Mingyang Ma¹

¹School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)

|May 11, 2024

Summary

This study introduces an improved YOLOv8n model for enhanced small object detection in remote sensing. The lightweight model significantly boosts accuracy in complex backgrounds while reducing parameters.

Keywords:

HPANet SSFF YOLOv8n remote sensing image small object detection

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Published on: May 7, 2019

Area of Science:

Computer Vision
Remote Sensing
Artificial Intelligence

Background:

Small object detection in remote sensing faces challenges like low resolution and occlusions.
Existing models like YOLOv8n struggle with integrating features from small objects.

Purpose of the Study:

To develop a lightweight and accurate model for small object detection in remote sensing images.
To improve the feature fusion mechanism for better small object recognition.

Main Methods:

A dedicated small object detection layer was added to the feature fusion network.
The Selective Spatial Feature Fusion (SSFF) module was introduced for multi-scale feature integration.
The Hierarchical Path Aggregation Network (HPANet) replaced the original Path Aggregation Network.

Main Results:

Achieved significant improvements in mean Average Precision (mAP) on VisDrone and AI-TOD datasets (up to 19.8% for mAP@0.5:0.95).
Reduced model parameters by 33% and model size by 31.7% compared to YOLOv8n.
Demonstrated quick and accurate identification of small objects in complex remote sensing imagery.

Conclusions:

The proposed lightweight model effectively addresses the challenges of small object detection in remote sensing.
The enhanced feature fusion and network structure lead to superior accuracy and efficiency.
This approach offers a promising solution for real-time remote sensing applications requiring precise small object identification.