How to get pavement distress detection ready for deep learning? A systematic approach

Eisenbach, Markus; Stricker, Ronny; Seichter, Daniel; Amende, Karl; Debes, Klaus; Sesselmann, Maximilian; Ebersbach, Dirk; Stoeckert, Ulrike; Groß, Horst–Michael

doi:10.1109/ijcnn.2017.7966101

Cited by 267 publications

(150 citation statements)

References 22 publications

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“…Although an image data set of the road surface exists, called the KITTI data set (Geiger et al., ), it is primarily used for applications related to automated driving. There is also the GAP data set for road damage detection with features of around 2,000 high‐resolution images with manually annotated damage (six classes) (Eisenbach et al., ). To the best of our knowledge, the GAP data set is the only publicly available data set for road damage detection.…”

Section: Related Workmentioning

confidence: 99%

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Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda

Sekimoto

Seto

et al. 2018

Computer aided Civil Eng

646

338

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Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).

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Section: Related Workmentioning

confidence: 99%

“…For example, the method proposed by Jo and Ryu (2015) detects only potholes in (Maeda et al, 2016;Zhang et al, 2016) only detects the presence or absence of damage. Further, although the GAPs data set (Eisenbach et al, 2017) treats the crack as a crack, in our data set, the cracks are classified into five types.…”

Section: Data Categorymentioning

confidence: 99%

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda

Sekimoto

Seto

et al. 2018

Computer aided Civil Eng

646

338

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“…There have been a few that have tried to analyze multiple distress categories and generate datasets of multiple types. A research team in Germany developed a CNN for pavement distress application based on imagery obtained through surveys across the German road network using a mobile mapping system attached to a vehicle [54,55]. Their team developed, the German Asphalt Pavement Distress (GAPS) dataset which was utilized to generate classifications based on six different distress categories based on the German road manuals with research ongoing utilizing their developed Neural network, ASVINOS.…”

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

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Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

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“…Good examples of such complex systems are ARAN 9000 developed by University of Catania and a mobile mapping system S.T.I.E.R. [12,51]. Both systems consist of several laser based measurement devices for texture analysis and range finding, as well as of several high speed cameras.…”

Section: Source Input Data and Data Collectionmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.

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How to get pavement distress detection ready for deep learning? A systematic approach

Cited by 267 publications

References 22 publications

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

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