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Related Experiment Video

Updated: Jan 13, 2026

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

ClearSight-RS: A YOLOv5-Based Network with Dynamic Enhancement for Remote Sensing Small Target Detection.

Jie Yuan^1,2, Shuyi Feng^1,2, Hao Han¹

¹College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210024, China.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

ClearSight-RS, an improved YOLOv5 network, enhances small target detection in remote sensing images by integrating novel modules for clearer feature perception and accurate localization. It significantly outperforms existing methods on benchmark datasets.

Keywords:

YOLOv5 attention mechanism dynamic enhancement remote sensing image small target detection target recognition

Related Experiment Videos

Last Updated: Jan 13, 2026

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

Area of Science:

Computer Vision
Remote Sensing
Artificial Intelligence

Background:

Small target detection in remote sensing is challenging due to complex backgrounds, weak features, and scale variations.
Existing methods struggle with accurately identifying and localizing small objects amidst clutter.

Purpose of the Study:

To develop an improved YOLOv5 network, ClearSight-RS, for enhanced small target detection in remote sensing.
To improve feature extraction, target focusing, and background suppression for small objects.

Main Methods:

Integration of an improved Dynamic Snake Convolution (DSConv) module in the backbone for boundary and texture feature extraction.
Embedding a Bi-Level Routing Attention (BRA) module in the Neck for better target focus and background suppression.
Optimization of the detection head by using shallow, high-resolution feature layers.

Main Results:

ClearSight-RS achieved the highest mAP for all 8 vehicle categories on the VEDAI dataset.
Achieved an overall mAP of 93.8% on the NWPU VHR-10 dataset, outperforming Faster RCNN and YOLOv5l.
Demonstrated the BRA module's effectiveness in suppressing background interference and capturing small target features on the DOTA dataset.

Conclusions:

ClearSight-RS effectively balances accuracy and efficiency for small target detection in complex remote sensing backgrounds.
The proposed network shows prominent performance in detecting vehicles and multi-category small targets.
The ClearSight-RS network validates its effectiveness for challenging remote sensing image analysis tasks.