Multi-scale feature fusion network for pixel-level pavement distress detection-Reference-Cited by-同舟云学术

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Multi-scale feature fusion network for pixel-level pavement distress detection

Published:2022-09 Issue: Volume:141 Page:104436
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Zhong Jingtao,Zhu Junqing,Huyan Ju,Ma Tao,Zhang Weiguang

Funder

Natural Science Foundation of Jiangsu Province

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference39 articles.

1. Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network;Zhang;Comput. Aided Civ. Infrastruct. Eng.,2019

2. Deep learning techniques for medical image segmentation: achievements and challenges;Hesamian;J. Digit. Imaging,2019

3. CE-Net: context encoder network for 2D medical image segmentation;Gu;IEEE Trans. Med. Imaging,2019

4. Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges;Feng;IEEE Trans. Intell. Transp. Syst.,2021

5. Real-time automated survey system of pavement cracking in parallel environment;Wang;J. Infrastruct. Syst.,2005

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1. A Generative Approach to Generalize Deep Learning Models for Pavement Distress Segmentation;2024-09-09

2. An investigation of texture-friction relationship with laboratory ring-shaped asphalt mixture specimens via close-range photogrammetry;Construction and Building Materials;2024-09

3. Review on monitoring of pavement subgrade settlement: Influencing factor, measurement and advancement;Measurement;2024-09

4. YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism;Journal of Real-Time Image Processing;2024-08-31

5. CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation;Automation in Construction;2024-08

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