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Related Experiment Video

Updated: Sep 10, 2025

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

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Published on: December 15, 2023

MSConv-YOLO: An Improved Small Target Detection Algorithm Based on YOLOv8.

Linli Yang^1,2, Barmak Honarvar Shakibaei Asli²

¹College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Journal of Imaging

|August 27, 2025

Summary

This study enhances YOLOv8s for detecting small objects in drone imagery using MultiScaleConv-YOLO (MSConv-YOLO). The improved model boosts detection accuracy and recall for small targets in complex aerial scenes.

Keywords:

MSConv-YOLO UAV aerial imagery WIoU small target detection

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Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Small object detection in Unmanned Aerial Vehicle (UAV) aerial imagery is challenging due to scale variations and complex backgrounds.
Existing frameworks like YOLOv8s require enhancements for optimal performance on small targets.

Purpose of the Study:

To improve the performance of small object detection in UAV aerial imagery.
To introduce practical engineering enhancements to the YOLOv8s framework.

Main Methods:

Developed MultiScaleConv-YOLO (MSConv-YOLO) by integrating a MultiScaleConv (MSConv) module for enhanced multi-scale feature extraction.
Replaced CIoU loss with WIoU v3 for improved bounding box regression of small targets.
Incorporated a high-resolution detection head in the neck-head structure to preserve fine-grained features.

Main Results:

MSConv-YOLO achieved a 6.9% improvement in mAP@0.5 and a 6.3% gain in recall compared to the baseline YOLOv8s on the VisDrone2019 dataset.
Ablation studies confirmed the effectiveness of individual enhancements.

Conclusions:

MSConv-YOLO offers a practical and effective solution for small object detection in UAV scenarios.
The proposed enhancements improve detection performance without fundamentally altering the YOLOv8s architecture.