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Related Experiment Video

Updated: Aug 5, 2025

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

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Published on: December 15, 2023

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection.

Chun Bao¹, Jie Cao^1,2, Qun Hao^1,2,3

¹Bionic Robot Key Laboratory of Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

Dual-YOLO enhances infrared object detection by integrating visible light features using a YOLOv7 framework. This approach significantly reduces false alarms and improves accuracy for critical applications like military reconnaissance and autonomous driving.

Keywords:

attention fusion dual-YOLO fusion loss fusion shuffle infrared object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing Technology

Background:

Infrared object detection faces challenges with high false alarm rates due to limited texture information, impacting overall accuracy.
Existing infrared detection networks struggle to meet the demands for low false alarms and high precision required in military remote sensing.

Purpose of the Study:

To develop an advanced infrared object detection network that overcomes the limitations of texture-less infrared images.
To improve the accuracy and reduce false detections in infrared object detection systems.

Main Methods:

Proposed Dual-YOLO network, a fusion of infrared and visible image features based on the YOLOv7 architecture.
Incorporated dual feature extraction channels, attention fusion, and fusion shuffle modules to optimize feature integration.
Integrated Inception and SE modules to enhance complementary image characteristics and designed a fusion loss function for efficient training.

Main Results:

Achieved 71.8% mean Average Precision (mAP) on the DroneVehicle dataset and 73.2% mAP on the KAIST dataset.
Demonstrated a detection accuracy of 84.5% on the FLIR dataset.
The Dual-YOLO network effectively reduced detection errors and improved overall performance.

Conclusions:

The Dual-YOLO network offers a significant improvement in infrared object detection accuracy and reliability.
The proposed architecture shows strong potential for applications in military reconnaissance, autonomous driving, and public safety.
Integrating multi-modal features is a promising direction for enhancing object detection in challenging environments.