DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions

DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions

Abstract In real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. Specifically, fog reduces visibility, complicating feature extraction and leading to detail loss, which impairs object localization and classificatio...

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Bibliographic Details
Main Authors: Zhiyong Jing, Zhaobing Chen, Yucheng Shi, Lei Shi, Lin Wei, Yufei Gao
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Object detection
Adverse weather
Image processing
Feature fusion
Online Access:https://doi.org/10.1038/s41598-025-03902-y
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https://doi.org/10.1038/s41598-025-03902-y

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