Policy Gradient–Based Focal Loss to Reduce False Negative Errors of Convolutional Neural Networks for Pavement Crack Segmentation-Reference-Cited by-同舟云学术

Policy Gradient–Based Focal Loss to Reduce False Negative Errors of Convolutional Neural Networks for Pavement Crack Segmentation

Published:2023-03 Issue:1 Volume:29 Page:
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Yang Enhui¹,Tang Youzhi²,Zhang Allen A.³^ORCID,Wang Kelvin C. P.⁴,Qiu Yanjun⁵^ORCID

Affiliation:

1. Associate Professor, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan 610031, China.

2. Graduate Research Assistant, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan 610031, China.

3. Associate Professor, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan 610031, China (corresponding author). ORCID: .

4. Professor, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74075.

5. Professor, School of Civil Engineering, Southwest Jiaotong Univ., Chengdu, Sichuan 610031, China. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JITSE4.ISENG-2157

Reference45 articles.

1. Ashok A. N. Rhinehart F. Beainy and K. M. Kitani. 2017. “N2n learning: Network to network compression via policy gradient reinforcement learning.” Preprint submitted September 18 2017. https://arxiv.org/abs/1709.06030.

2. Evaluating pavement cracks with bidimensional empirical mode decomposition;Ayenu-Prah A.;EURASIP J. Adv. Signal Process.,2008

3. Bray J. B. Verma X. Li and W. He. 2006. “A neural network based technique for automatic classification of road cracks.” In Proc. 2006 IEEE Int. Joint Conf. on Neural Network Proceedings 907–912. New York: IEEE.

4. Review of Pavement Defect Detection Methods

5. Chaurasia A. and E. Culurciello. 2017. “Linknet: Exploiting encoder representations for efficient semantic segmentation.” In Proc. 2017 IEEE Visual Communications and Image Processing (VCIP) 1–4. New York: IEEE.

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