Asphalt Road Quality Classification Based on CNN Architecture VGG16
Author:
Affiliation:
1. Universitas Dian Nuswantoro,Faculty of Computer Science,Semarang,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10294330/10295274/10295326.pdf?arnumber=10295326
Reference13 articles.
1. Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation
2. Crack detection and classification in asphalt pavement images using deep convolution neural network;yusof;Proc - 8th IEEE Int Conf Control Syst Comput Eng ICCSCE 2018,2019
3. Automatic Pavement Crack Detection and Classification Using Multiscale Feature Attention Network
4. Automated Brain Image Classification Based on VGG-16 and Transfer Learning
5. A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors
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