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A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information.

Jing Li^1,2, Haochen Sun², Zhiyong Zhang¹

¹Information Engineering College, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This study introduces I-YOLO-V5, an improved object detection framework for remote sensing images. The new model significantly enhances the detection of small objects, improving accuracy and reducing missed detections in high-altitude imagery.

Keywords:

RSI information YOLO network densely connected network residual network small-object detection

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Area of Science:

Remote Sensing
Computer Vision
Artificial Intelligence

Background:

Remote sensing images (RSIS) are crucial for environmental monitoring, with object detection (OD) being a key technology.
Existing OD algorithms often struggle with small objects, leading to poor detection precision and high miss rates.
High-altitude RSIS present unique challenges for accurate object detection.

Purpose of the Study:

To develop an improved object detection framework, I-YOLO-V5, specifically for high-altitude remote sensing images.
To address the limitations of current algorithms in detecting small objects within complex environments.
To enhance the overall performance of object detection in remote sensing applications.

Main Methods:

Proposed I-YOLO-V5 framework incorporating residual network units for enhanced feature extraction.
Integrated densely connected networks to mitigate gradient fading issues.
Introduced a fourth detection layer to specifically improve small-object detection capabilities.

Main Results:

The I-YOLO-V5 framework demonstrated a 15.4% improvement in average accuracy compared to existing advanced OD algorithms.
Achieved a 46.8% reduction in the miss rate for small objects on the RSOD dataset.
Effectiveness verified for detecting small objects in complex, high-altitude remote sensing environments.

Conclusions:

I-YOLO-V5 offers a significant advancement in object detection for high-altitude remote sensing images.
The framework effectively overcomes the challenges associated with detecting small objects.
This improved performance has critical implications for various remote sensing applications.