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IV-YOLO: A Lightweight Dual-Branch Object Detection Network.

Dan Tian¹, Xin Yan¹, Dong Zhou¹

¹Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This study introduces IV-YOLO, a novel object detection network combining visible light and infrared images for improved environmental perception. The multimodal approach enhances accuracy and real-time performance in challenging conditions.

Keywords:

IV-YOLO attention mechanism bi-directional pyramid feature fusion dual-branch image object detection small target detection

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Single-modality object detection struggles with environmental variations (lighting, weather, obstructions).
Limitations in adaptability and accuracy hinder applications like surveillance and autonomous driving.
Integrating visible light and infrared imaging offers complementary data for robust perception.

Purpose of the Study:

To develop an object detection network (IV-YOLO) that effectively fuses visible light and infrared image features.
To enhance environmental adaptability and detection accuracy compared to existing single-modality methods.
To achieve high real-time performance with reduced model complexity.

Main Methods:

Proposed IV-YOLO based on YOLOv8 with a dual-branch fusion structure.
Implemented Bidirectional Pyramid Feature Fusion (Bi-Fusion) for effective multimodal feature integration.
Developed a Shuffle-SPP structure with channel and spatial attention for deep feature enhancement.
Designed a tailored loss function for multi-scale object detection and faster convergence.

Main Results:

IV-YOLO demonstrated mAP improvements of 2.8% (Drone Vehicle), 1.1% (FLIR), and 2.2% (KAIST) over Dual-YOLO.
Achieved 203.2 fps on Drone Vehicle and FLIR datasets with only 4.31 M parameters.
Significantly outperformed YOLOv8n and YOLO-FIR in performance and parameter efficiency.

Conclusions:

IV-YOLO effectively integrates multimodal features for superior object detection.
The network offers high real-time performance and lower parameter complexity.
IV-YOLO shows significant promise for autonomous driving, security surveillance, and remote sensing applications.