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WEDGE-Net: Wavelet-Driven Memory-Efficient Anomaly Detection for Industrial Edge Computing.

Joon-Min Park1, Gye-Young Kim1

  • 1School of Software, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
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WEDGE-Net enhances anomaly detection (AD) for edge devices by efficiently filtering noise and extracting structural features. This novel approach achieves high accuracy and speed, making it ideal for real-time industrial inspection.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning-based anomaly detection (AD) faces challenges in industrial edge deployment due to memory and computational constraints.
  • Existing methods often prioritize accuracy over operational efficiency, neglecting real-world factors like noise and latency.

Purpose of the Study:

  • To develop an efficient and accurate anomaly detection model for resource-constrained edge devices in industrial settings.
  • To address the limitations of current AD approaches by balancing structural precision with extreme memory efficiency.

Main Methods:

  • Introduced WEDGE-Net, a dual-stream architecture decoupling anomaly detection into Frequency (DWT) and Context streams.
  • The Frequency Stream filters environmental noise, while the Context Stream uses a Semantic Module for feature extraction and object consistency.
Keywords:
discrete wavelet transformindustrial edge computingmemory efficiencynoise robustnessreal-time inspectionunsupervised anomaly detection

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  • Synthesized both streams to suppress noise and enhance structural feature compactness.

    • Main Results:

    • WEDGE-Net achieved 97.82% mean image-level AUROC on the MVTec AD dataset with 1% memory compression.
    • Demonstrated superior noise resistance in robustness analysis on the 'Tile' category.
    • Achieved 686.5 FPS inference speed on an RTX 4090 GPU, with a 2.1x speedup over PatchCore-10% while maintaining competitive accuracy.

    Conclusions:

    • WEDGE-Net offers a practical solution for real-time industrial inspection on edge devices.
    • The model effectively balances high detection accuracy with extreme memory efficiency and operational speed.
    • This work provides a valuable reference for deploying advanced AD systems in manufacturing environments.