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PotholeEye^(+): Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance 被引量:1
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作者 Juyoung Park jung hee lee Junseong Bang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第6期965-976,共12页
We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extr... We propose a mobile system,called PotholeEye+,for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video.PotholeEye+pre-processes the images,extracts features,and classifies the distress into a variety of types,while the road manager is driving.Every day for a year,we have tested PotholeEye+on real highway involving real settings,a camera,a mini computer,a GPS receiver,and so on.Consequently,PotholeEye+detected the pavement distress with accuracy of 92%,precision of 87%and recall 74%averagely during driving at an average speed of 110 km/h on a real highway. 展开更多
关键词 Pavement distress DETECTION CLASSIFICATION convolutional neural network deep learning video analysis
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