A novel MO-YOLOv4 for segmentation of multi-class bridge damages-Reference-Cited by-同舟云学术

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A novel MO-YOLOv4 for segmentation of multi-class bridge damages

Published:2024-10 Issue: Volume:62 Page:102586
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

He Zengsheng,Su Cheng^ORCID,Deng Yichuan^ORCID

Funder

Guangdong Provincial Natural Science Foundation

Guangzhou Municipal Science and Technology Program key projects

Basic and Applied Basic Research Foundation of Guangdong Province

South China University of Technology Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference51 articles.

1. ImageNet classification with deep convolutional neural networks;Krizhevsky;Commum. ACM,2017

2. Deep learning-based crack damage detection using convolutional neural networks;Cha;Comput. Aided Civ. Inf. Eng.,2017

3. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique;Kim;Sensors,2018

4. S.Q. Ren, K.M. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, in: 29th Annual Conference on Neural Information Processing Systems (NIPS), Montreal, Canada, 2015, pp. 1137-1149, https://doi.org/10.1109/TPAMI.2016.2577031.

5. J. Redmon, S. Divvala, R. Girshick, A. Farhadi. You Only Look Once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, LasVegas, Nevada, USA, 2016, pp. 779-788, https://doi.org/10.1109/CVPR.2016.91.

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