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Related Experiment Video

Updated: May 25, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

A detection method for dense emitters based on a separation and boundary-aware collaborative enhancement detection

Ziyi Zhang¹, Youchen Fan², Shunhu Hou¹

¹Graduate School, Space Engineering University, Beijing, 101416, China.

Scientific Reports

|May 23, 2026

Summary

This summary is machine-generated.

This study introduces a new network to improve radio emitter detection. The Separation and Boundary-Aware Collaborative Enhancement Detection Network (SBCE-Net) enhances precision and recall for dense emitters.

Keywords:

Dense emitter detection Feature confusion Precision-recall balance Radio environment map Separation and Boundary-aware Collaborative enhancement boundary ambiguity

Related Experiment Videos

Last Updated: May 25, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Radio frequency engineering
Computer vision
Machine learning

Background:

Low detection efficiency of dense emitters in radio environment maps is a significant challenge.
Feature confusion and boundary ambiguity lead to merged targets, missed detections, and false alarms, creating a precision-recall dilemma.

Purpose of the Study:

To propose a novel network, the Separation and Boundary-Aware Collaborative Enhancement Detection Network (SBCE-Net), to address the low detection efficiency of dense emitters.
To improve the precision-recall balance in radio environment mapping.

Main Methods:

Developed a collaborative enhancement detection network with two modules: one for separating confused features and another for restoring blurred boundaries.
Trained and evaluated the network on a dense emitter dataset constructed from RadioMapSeer.

Main Results:

SBCE-Net achieved an F1 score of 0.988, recall of 0.991, and precision of 0.985, outperforming seven existing methods.
The method demonstrated consistent high performance across various confidence thresholds and emitter densities.
Ablation studies confirmed the complementary contributions of the network's modules.

Conclusions:

The proposed SBCE-Net effectively overcomes feature confusion and boundary ambiguity in dense emitter detection.
The collaborative design of the separation and boundary restoration modules enhances overall detection performance and precision-recall balance.
SBCE-Net offers a robust solution for improving radio environment mapping accuracy.