LID-Net: A lightweight image dehazing network for automatic driving vision systems-Reference-Cited by-同舟云学术

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LID-Net: A lightweight image dehazing network for automatic driving vision systems

Published:2024-11 Issue: Volume:154 Page:104673
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Tao Fazhan,Chen Qi,Fu Zhigao,Zhu Longlong,Ji Baofeng^ORCID

Publisher

Elsevier BV

Reference58 articles.

1. System integration of sensor-fusion localization tasks using vision-based driving lane detection and road-marker recognition;Lin;IEEE Syst. J.,2020

2. TBDB: token bucket-based dynamic batching for resource scheduling supporting neural network inference in intelligent consumer electronics;Gao;IEEE Trans. Consum. Electron.,2024

3. THCANet: two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images;Xu;Digit. Signal Process.,2023

4. G-NET: accurate lane detection model for autonomous vehicle;Wang;IEEE Syst. J.,2023

5. Object detection and activity recognition in video surveillance using neural networks;Payghode;Int. J. Web Inf. Syst.,2023

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