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Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Learnable edge detectors can make deep convolutional neural networks more robust.

Jin Ding1,2, Jie-Chao Zhao1, Yong-Zhi Sun1

  • 1School of Automation and Electrical Engineering & Key Institute of Robotics of Zhejiang Province, Zhejiang University of Science and Technology, Hangzhou, China.

Plos One
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Binary Edge Feature Branch (BEFB) to enhance deep convolutional neural networks (DCNNs). BEFB improves DCNN robustness against adversarial attacks by integrating shape and texture features, crucial for safety-critical applications.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep convolutional neural networks (DCNNs) are susceptible to adversarial perturbations, posing risks in safety-critical applications like autonomous driving.
  • Enhancing DCNN robustness is vital for reliable real-world deployment.

Purpose of the Study:

  • To develop a novel method for improving DCNN robustness by incorporating shape-based features.
  • To propose a Binary Edge Feature Branch (BEFB) that learns binary edge features.

Main Methods:

  • Designed four learnable edge detectors as kernels for Sobel layers.
  • Proposed a BEFB comprising Sobel and threshold layers to extract binary edge features.
  • Integrated BEFB with popular backbones (VGG16, ResNet34) and combined edge features with texture features for classification.

Main Results:

  • The BEFB is lightweight and does not negatively impact training.
  • BEFB-integrated models showed improved accuracy against white-box and black-box adversarial attacks.
  • BEFB-integrated models with robustness techniques outperformed original models in classification accuracy.

Conclusions:

  • This work demonstrates the feasibility of enhancing DCNN robustness by combining shape-like and texture features.
  • The BEFB offers an effective and efficient approach to improve DCNN resilience.