路径洞检测,以帮助视障人士导航

Md. Milon Islam, M. S. Sadi
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引用次数: 16

摘要

随着人口的快速增长,视障人士的数量也在不断增加。视障人士由于失去视力,在日常生活中面临许多困难。小路上的洞是他们行走的主要障碍。因此,路径孔检测已成为辅助视障人士的一个突出问题。提出了一种利用卷积神经网络检测路面孔洞的解决方案。该系统能够对具有路径孔和非路径孔的路面进行分类。我们使用了两个基准标记数据集,分别是KITTI ROAD和Pothole detection。我们用训练-测试分割法训练卷积神经网络。该系统的性能指标包括准确率、精密度、召回率和错误率。在20次迭代的测试阶段,系统获得的总体精度和误差分别为97.12%和0.065。在测试阶段,系统获得的查准率和查全率分别为96.68%和95.77%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Path Hole Detection to Assist the Visually Impaired People in Navigation
The number of visually impaired people is increasing with the rapid growth of population. The visually impaired people face much difficulties in their daily living owing to losing their vision. Path hole is a major hindrance to their walking. So path hole detection has become a prominent issue to aid the visually impaired people. We proposed a solution by detecting path hole on the road surfaces using Convolution Neural Network. The proposed system is able to classify the road surfaces with path hole and non-path hole. We have used two bench marked dataset named as KITTI ROAD and Pothole detection. We have trained Convolution Neural Network with training-testing partition. The performance of the system is measured in terms of accuracy, precision, recall and error rate. The overall accuracy and error obtained by the system are 97.12% and 0.065 respectively in testing phase for 20 iterations. Additionally, precision and recall obtained by the system are 96.68% and 95.77% in testing phase.
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