학술논문

A Deep Learning-Based Approach for Road Pothole Detection in Timor Leste
Document Type
Conference
Source
2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) Service Operations and Logistics, and Informatics (SOLI), 2018 IEEE International Conference on. :279-284 Jul, 2018
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Roads
Machine learning
Feature extraction
Training
Support vector machines
Convolutional neural networks
Task analysis
Potholes
Deep Learning
Convolutional Neural Network
Image Classification
Language
Abstract
This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.