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Updated: Dec 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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DSNet: Joint Semantic Learning for Object Detection in Inclement Weather Conditions.

Shih-Chia Huang, Trung-Hieu Le, Da-Wei Jaw

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 10, 2020
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    Summary
    This summary is machine-generated.

    This study introduces a novel dual-subnet network (DSNet) for improved object detection in fog. DSNet enhances visibility and object detection accuracy, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Object detection using convolutional neural networks is prevalent in computer vision.
    • Detecting objects in adverse weather, particularly fog, remains a significant challenge due to reduced visibility.

    Purpose of the Study:

    • To address the limitations of current object detection methods in foggy conditions.
    • To develop a novel network for simultaneous visibility enhancement and object detection.

    Main Methods:

    • Introduction of a Dual-Subnet Network (DSNet) trained end-to-end.
    • DSNet jointly learns visibility enhancement, object classification, and localization.
    • Utilizes RetinaNet as the detection subnet and incorporates a feature recovery module in the restoration subnet.

    Main Results:

    • DSNet achieved 50.84% mAP on a synthetic foggy dataset.
    • DSNet achieved 41.91% mAP on the Foggy Driving dataset.
    • Outperformed state-of-the-art object detectors and dehazing-detection combination models.

    Conclusions:

    • DSNet offers a significant improvement for object detection in foggy environments.
    • The proposed method effectively enhances visibility and maintains high detection speed.
    • DSNet presents a promising approach for real-world applications requiring robust object detection in adverse weather.