Camera-Based Road Damage Detection System with Edge Detection Algorithm

Nahari, Rosida Vivin; Alfita, Riza; Anam, Hairul; Wibisono, Kunto Aji; Pramudia, Mirza

doi:10.4108/eai.24-10-2018.2280530

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Nguyen et al (25) reviewed the current state-of-the-art methods and proposed key focus areas for future research. Prior work has focused on improving the performance of detection systems for MMS, such as the introduction of pothole patrol (26), laser line striper sensor (27), and image processing algorithms (28). To reduce cost, many research studies proposed a cheap alternate version of MMS by utilizing smartphone images and different deep learning models, including a lightweight single-state feedforward convolutional network, single-shot multibox detector model (2,29,30), and Darknet53 model finetuned using the YOLO version 3 framework (31).…”

Section: Related Workmentioning

confidence: 99%

Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Truong

Mora

Cheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Surface distress is an indication of poor or unfavorable pavement performance or signs of impending failure that can be classified into a fracture, distortion, or disintegration. To mitigate the risk of failing roadways, effective methods to detect road distress are needed. Recent studies associated with the detection of road distress using object detection algorithms are encouraging. Although current methodologies are favorable, some of them seem to be inefficient, time-consuming, and costly. For these reasons, the present study presents a methodology based on the mask regions with convolutional neural network model, which is coupled with the new object detection framework Detectron2 to train the model that utilizes roadway imagery acquired from an unmanned aerial system (UAS). For a comprehensive understanding of the performance of the proposed model, different settings are tested in the study. First, the deep learning models are trained based on both high- and low-resolution datasets. Second, three different backbone models are explored. Finally, a set of threshold values are tested. The corresponding experimental results suggest that the proposed methodology and UAS imagery can be used as efficient tools to detect road distress with an average precision score up to 95%.

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Section: Related Workmentioning

confidence: 99%

Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Truong

Mora

Cheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Accuracy and MAP generated from Wrapper method with d = 1 which are 55.61% and 0.710 respectively [4]. In another study [9] by comparing the algorithm to the texture features of Gray Level Cooccurrence Matrix (GLCM) and Threshold-based marking , the test shows that the accuracy of the road damage system is 91.67% with a processing time of 0.08% seconds for each frame carried out.…”

Section: Introductionmentioning

confidence: 97%

Implementation of KNN Methods And GLCM Extraction For Classification Of Road Damage Level

Lubis

Iskandar

Panjaitan

2022

itsdi

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Road damage that occurs on several road surfaces causes huge losses, especially for road users such as travel time, congestion, accidents and others , so it is necessary to assess the level of road damage. At this time, problems in determining the level of road damage such as detecting cracks, potholes, calculating the width of cracks, the percentage of cracks and generating the level of road damage are still carried out by slow manual calculations using the Surface method. Distress Index (SDI). In this study, the KNN and GLCM methods will be used to detect road damage. Based on the results of the tests carried out, the accuracy of the results of disease detection with the KNN method and GLCM extraction depends on the number of datasets contained in the system. The process of measuring the level of road damage to get the results of the level of damage to the road can be done quickly, namely by entering a road damage image into the application.

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Camera-Based Road Damage Detection System with Edge Detection Algorithm

Cited by 2 publications

References 3 publications

Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Implementation of KNN Methods And GLCM Extraction For Classification Of Road Damage Level

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