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Related Experiment Video

Updated: Sep 6, 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

YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds.

Xiao Zhou¹, Lang Jiang¹, Caixia Hu²

¹School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|June 24, 2022

Summary

This study introduces YOLO-SASE, an improved algorithm for detecting infrared small targets. It enhances detection accuracy and recall rates in complex backgrounds, improving overall performance.

Keywords:

adaptive channel attention infrared small target detection super-resolution reconstruction

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Area of Science:

Computer Vision
Artificial Intelligence
Signal Processing

Background:

Detecting small infrared targets in complex backgrounds presents significant challenges.
Existing algorithms often struggle with low contrast and cluttered environments.

Purpose of the Study:

To enhance the detection capabilities for infrared small targets.
To improve the accuracy, recall, and stability of target detection algorithms.

Main Methods:

An improved detection algorithm, YOLO-SASE, was developed based on the YOLO detection framework and SRGAN network.
The algorithm incorporates super-resolution reconstructed images, SASE module, SPP module, and a multi-level receptive field structure.
Feature weight exploration was used to adjust detection output layers and improve feature utilization efficiency.

Main Results:

The YOLO-SASE algorithm demonstrated a 2% improvement in accuracy and a 3% improvement in recall rate compared to the original model.
Significant improvements in the stability of results during the training process were observed.
The algorithm effectively handles complex backgrounds for infrared small target detection.

Conclusions:

The proposed YOLO-SASE algorithm offers a robust solution for infrared small target detection.
The integration of super-resolution and specialized modules enhances detection performance and stability.
This work contributes to advancements in computer vision for challenging detection tasks.