基于深度学习的东帝汶道路坑洞检测方法

2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) Pub Date : 2018-07-01 DOI:10.1109/SOLI.2018.8476795

Vosco Pereira, S. Tamura, S. Hayamizu, Hidekazu Fukai

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引用次数: 55

摘要

本研究提出了一种基于卷积神经网络(CNN)的低成本道路坑洼图像检测方法。我们的模型完全是在从几个不同的地方收集的图像上进行训练的，这些图像在潮湿、干燥和阴凉的条件下都有变化。使用500张测试图像的实验表明，我们的模型可以同时达到99.80%的准确率、100%的精密度、99.60%的召回率和99.60%的F-Measure值。

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A Deep Learning-Based Approach for Road Pothole Detection in Timor Leste

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.

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2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

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