Optimization and sensitivity analysis of existing deep learning models for pavement surface monitoring using low-quality images-Reference-Cited by-同舟云学术

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Optimization and sensitivity analysis of existing deep learning models for pavement surface monitoring using low-quality images

Published:2022-08 Issue: Volume:140 Page:104332
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Roberts Ronald,Menant Fabien,Di Mino Gaetano,Baltazart Vincent

Publisher

Elsevier BV

Subject

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

Reference58 articles.

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2. Rapport;Laboratoire Central des Ponts et Chaussees (LCPC), Relevé des dégradations de surface des chaussées: Méthode d'’essai LPC n°38-2,1997

3. A survey of deep neural network architectures and their applications;Liu;Neurocomputing,2017

4. Applied AI in instrumentation and measurement: the deep learning revolution;Khanafer;Inst. Elect. Electron. Eng. Instrum. Meas. Mag.,2020

5. Instrumented infrastructures for damage detection and management;Fedele,2017

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1. LiDAR-Based Automatic Pavement Distress Detection and Management Using Deep Learning and BIM;Journal of Construction Engineering and Management;2024-07

2. Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation;Automation in Construction;2024-02

3. Mechanical insights into the behavior of cement stabilized aggregates during compaction and failure using smart aggregate: Experiments and DEM simulations;Construction and Building Materials;2023-10

4. Synergizing Low Rank Representation and Deep Learning for Automatic Pavement Crack Detection;IEEE Transactions on Intelligent Transportation Systems;2023-10

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