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Updated: Mar 21, 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

A CNN-transformer dual-branch network with structure-aware loss for high-resolution edge detection.

Jinhao Jiang¹, Junhao Guo¹, Zijing Yang^2,3

¹School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing, 102600, China.

Scientific Reports

|March 20, 2026

Summary

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Reducing Line Loss

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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This study introduces a novel Edge-Structure-Aware Loss Function to improve deep learning edge detection. It addresses limitations of pixel-wise losses by enforcing edge geometric properties, leading to more coherent and accurate edge maps.

Area of Science:

Computer Vision
Deep Learning
Image Processing

Background:

Edge detection is crucial for computer vision tasks like segmentation and object detection.
Current deep learning methods use pixel-wise losses (e.g., Binary Cross Entropy) that ignore edge geometry.
This pixel-level supervision leads to artifacts like fragmented or blurred edges.

Purpose of the Study:

To develop a novel loss function that explicitly supervises edge geometric properties.
To improve the structural coherence and accuracy of deep learning-based edge detection.
To address the limitations of existing pixel-wise loss functions in edge detection.

Main Methods:

Proposed a novel Edge-Structure-Aware Loss Function incorporating gradient, continuity, and directional consistency constraints.

Related Experiment Videos

Last Updated: Mar 21, 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

Introduced a Three-Stage Dynamic Loss Scheduling Strategy using curriculum learning.

Designed a dual-branch network architecture integrating semantic and contextual features.

Main Results:

Achieved competitive performance on multiple benchmarks, including BSDS500 (ODS=0.847/OIS=0.861), Multicue (ODS=0.899/OIS=0.907), and NYUDv2 (ODS=0.761/OIS=0.776).
Demonstrated significant improvements in edge map quality by addressing fragmentation and coherence issues.
Validated the effectiveness of the proposed structure-aware loss and scheduling strategy.

Conclusions:

The proposed Edge-Structure-Aware Loss Function effectively enhances edge detection accuracy and structural integrity.
The dynamic loss scheduling and dual-branch architecture provide a robust framework for structure-aware edge learning.
This work offers a significant advancement over traditional pixel-wise loss functions in computer vision.