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Related Experiment Video

Updated: Jul 11, 2025

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

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

Published on: December 15, 2023

Aerial images object detection method based on cross-scale multi-feature fusion.

Yang Pan¹, Jinhua Yang¹, Lei Zhu¹

¹School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China.

Mathematical Biosciences and Engineering : MBE

|November 3, 2023

Summary

This study introduces CMF-YOLOv5s, an improved aerial image target detection method. It enhances small target detection accuracy and speed in complex backgrounds, outperforming existing lightweight networks.

Keywords:

YOLOv5s aerial images cross-scale multi-feature fusion object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Aerial image analysis is crucial for navigation, traffic control, and environmental monitoring.
Detecting small targets in complex aerial backgrounds presents significant challenges for algorithms.
Existing lightweight networks struggle with accuracy and real-time performance for small aerial targets.

Purpose of the Study:

To enhance the detection accuracy of lightweight networks for small targets in aerial imagery.
To develop a novel cross-scale multi-feature fusion method for improved aerial target detection.
To address the high leakage detection rate and improve recognition of small targets.

Main Methods:

Proposed a cross-scale multi-feature fusion target detection method (CMF-YOLOv5s) based on YOLOv5s.
Introduced a bidirectional cross-scale feature fusion sub-network (BsNet) with a multi-scale fusion module (MFF).
Implemented a multi-scale detection head with four outputs and optimized anchor boxes using a genetic algorithm with K-means.

Main Results:

CMF-YOLOv5s achieved a detection speed of 116 FPS on the VisDrone-2019 dataset.
Demonstrated significant improvements in mAP0.5 (5.5%) and mAP0.5:0.95 (3.6%) for small targets compared to the original YOLOv5s.
Outperformed eight advanced lightweight networks, with mAP0.5 and mAP0.5:0.95 improvements exceeding 3.3% and 1.9%, respectively.

Conclusions:

The proposed CMF-YOLOv5s method effectively enhances small target detection in complex aerial images.
The integration of BsNet, MFF, and optimized anchor boxes improves feature fusion and small target perception.
CMF-YOLOv5s offers a superior balance of accuracy and speed for aerial image target detection applications.