YOLOv7-RDD: A Lightweight Efficient Pavement Distress Detection Model-Reference-Cited by-同舟云学术

YOLOv7-RDD: A Lightweight Efficient Pavement Distress Detection Model

Published:2024-07 Issue:7 Volume:25 Page:6994-7003
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ning Zhipeng¹,Wang Hui²^ORCID,Li Shenglin¹,Xu Zhoucong³

Affiliation:

1. College of Artificial Intelligence, Southwest University, Chongqing, China

2. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering, Chongqing University, Chongqing, China

3. China Merchants Chongqing Communications Technology Research and Design Institute Company Ltd., Chongqing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10582787/10401012.pdf?arnumber=10401012

Reference47 articles.

1. The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

2. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

3. RoadID: A Dedicated Deep Convolutional Neural Network for Multipavement Distress Detection

4. Cross‐scene pavement distress detection by a novel transfer learning framework

5. Deep Metric Learning-Based for Multi-Target Few-Shot Pavement Distress Classification

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2. A Method for Surveying Road Pavement Distress Based on Front-View Image Data Using a Lightweight Segmentation Approach;Journal of Computing in Civil Engineering;2024-09

3. Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone;Buildings;2024-05-27

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