• Title/Summary/Keyword: Pothole Detection

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2D LiDAR based 3D Pothole Detection System (2차원 라이다 기반 3차원 포트홀 검출 시스템)

  • Kim, Jeong-joo;Kang, Byung-ho;Choi, Su-il
    • Journal of Digital Contents Society
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    • v.18 no.5
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    • pp.989-994
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    • 2017
  • In this paper, we propose a pothole detection system using 2D LiDAR and a pothole detection algorithm. Conventional pothole detection methods can be divided into vibration-based method, 3D reconstruction method, and vision-based method. Proposed pothole detection system uses two inexpensive 2D LiDARs and improves pothole detection performance. Pothole detection algorithm is divided into preprocessing for noise reduction, clustering and line extraction for visualization, and gradient function for pothole decision. By using gradient of distance data function, we check the existence of a pothole and measure the depth and width of the pothole. The pothole detection system is developed using two LiDARs, and the 3D pothole detection performance is shown by detecting a pothole with moving LiDAR system.

Pothole Detection Method in Asphalt Pavement (아스팔트 도로의 포트홀 검출 방법)

  • Kim, Young-Ro;Kim, Taehyeong;Ryu, Seungki
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.10
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    • pp.248-255
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    • 2014
  • In this paper, we propose a pothole detection method in asphalt pavement using various features. Segmentation, candidate, and decision steps of pothole detection are processed according to the values which are derived from feature characteristics. Segmentation step, we use histogram and closing operation of morphology filter which extracts dark regions for pothole detection. Candidate step, we extract candidate regions of pothole using various features such as size, compactness, etc. Finally, decision step, candidate regions are decided whether pothole or not using comparison of pothole and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination of pothole and similar patterns.

Real Time Pothole Detection System based on Video Data for Automatic Maintenance of Road Surface Distress (도로의 파손 상태를 자동관리하기 위한 동영상 기반 실시간 포트홀 탐지 시스템)

  • Jo, Youngtae;Ryu, Seungki
    • KIISE Transactions on Computing Practices
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    • v.22 no.1
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    • pp.8-19
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    • 2016
  • Potholes are caused by the presence of water in the underlying soil structure, which weakens the road pavement by expansion and contraction of water at freezing and thawing temperatures. Recently, automatic pothole detection systems have been studied, such as vibration-based methods and laser scanning methods. However, the vibration-based methods have low detection accuracy and limited detection area. Moreover, the costs for laser scanning-based methods are significantly high. Thus, in this paper, we propose a new pothole detection system using a commercial black-box camera. Normally, the computing power of a commercial black-box camera is limited. Thus, the pothole detection algorithm should be designed to work with the embedded computing environment of a black-box camera. The designed pothole detection algorithm has been tested by implementing in a black-box camera. The experimental results are analyzed with specific evaluation metrics, such as sensitivity and precision. Our studies confirm that the proposed pothole detection system can be utilized to gather pothole information in real-time.

Pothole Detection Algorithm Based on Saliency Map for Improving Detection Performance (포트홀 탐지 정확도 향상을 위한 Saliency Map 기반 포트홀 탐지 알고리즘)

  • Jo, Young-Tae;Ryu, Seung-Ki
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.4
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    • pp.104-114
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    • 2016
  • Potholes have caused diverse problems such as wheel damage and car accident. A pothole detection technology is the most important to provide efficient pothole maintenance. The previous pothole detections have been performed by manual reporting methods. Thus, the problems caused by potholes have not been solved previously. Recently, many pothole detection systems based on video cameras have been studied, which can be implemented at low costs. In this paper, we propose a new pothole detection algorithm based on saliency map information in order to improve our previously developed algorithm. Our previous algorithm shows wrong detection with complicated situations such as the potholes overlapping with shades and similar surface textures with normal road surfaces. To address the problems, the proposed algorithm extracts more accurate pothole regions using the saliency map information, which consists of candidate extraction and decision. The experimental results show that the proposed algorithm shows better performance than our previous algorithm.

Pothole Detection using Intensity and Motion Information (명암과 움직임 정보를 이용한 포트홀 검출)

  • Kim, Young-Ro;Jo, Youngtae;Ryu, Seungki
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.11
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    • pp.137-146
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    • 2015
  • In this paper, we propose a pothole detection method using various features of intensity and motion. Segmentation, decision steps of pothole detection are processed according to the values which are derived from feature characteristics. For segmentation using intensity, we use a binarization method using histogram to separate pothole region from background. For segmentation using motion, we filter using high pass filter and get standard deviation value. This value is divided by regression value according to camera environment such as photographing angle, height, velocity, etc. We get binary image by histogram based binarization. For decision, candidate regions are decided whether pothole or not using comparison of candidate and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination between pothole and similar patterns.

Autonomous pothole detection using deep region-based convolutional neural network with cloud computing

  • Luo, Longxi;Feng, Maria Q.;Wu, Jianping;Leung, Ryan Y.
    • Smart Structures and Systems
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    • v.24 no.6
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    • pp.745-757
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    • 2019
  • Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

A Study on Algorithm for Materials Take-off Using Pothole Detection System (포트홀 감지 시스템을 이용한 보수재료량 산출 알고리즘 개발)

  • Kim, Kyungnam;Kim, Sung-Ho;Kim, Nakseok
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.37 no.3
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    • pp.603-610
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    • 2017
  • Various type of pavement deterioration such as crack, bumpy, pothole is rapidly increasing according to the accelerated environmental changes like heavy rainfall, frequent snowing, difference temperature, etc. Accident related to pothole that cause fatal traffic accidents has been increased more than five times over the next five years starting from 2008. As direct or indirect damage by pothole which caused injuries and car damages increases every year, quicker and more efficient management measures are necessary. This study presents the algorithm for materials quantity take-off. The algorithm was suggested by correlation in pothole size and area. Suggested algorithm were confirmed the validity through the 15 field survey in capital area. According to the results of survey, usually the residual materials at which 5~7 kg was generated decreased to 1~2 kg. It showed that automatic pothole detection system is expected not only to reduce materials and resources, but also to contribute to quality improvements of pavement through more accurate material take-off from the situation of constructing rely on their own judgement.

Proposed Pre-Processing Method for Improving Pothole Dataset Performance in Deep Learning Model and Verification by YOLO Model (딥러닝 모델에서 포트홀 데이터셋의 성능 향상을 위한 전처리 방법 제안과 YOLO 모델을 통한 검증)

  • Han-Jin Lee;Ji-Woong Yang;Ellen J. Hong
    • Journal of the Institute of Convergence Signal Processing
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    • v.23 no.4
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    • pp.249-255
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    • 2022
  • Potholes are an important clue to the structural defects of asphalt pavement and cause many casualties and property damage. Therefore, accurate pothole detection is an important task in road surface maintenance. Many machine learning technologies are being introduced for pothole detection, and data preprocessing is required to increase the efficiency of deep learning models. In this paper, we propose a preprocessing method that emphasizes important textures and shapes in pothole datasets. The proposed preprocessing method uses intensity transformation to reduce unnecessary elements of the road and emphasize the texture and shape of the pothole. In addition, the feature of the porthole is detected using Superpixel and Sobel edge detection. Through performance comparison between the proposed preprocessing method and the existing preprocessing method, it is shown that the proposed preprocessing method is a more effective method than the existing method in detecting potholes.

Development and Evaluation of Automatic Pothole Detection Using Fully Convolutional Neural Networks (완전 합성곱 신경망을 활용한 자동 포트홀 탐지 기술의 개발 및 평가)

  • Chun, Chanjun;Shim, Seungbo;Kang, Sungmo;Ryu, Seung-Ki
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.5
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    • pp.55-64
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    • 2018
  • In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

Deep Learning-based Pothole Detection System (딥러닝을 이용한 포트홀 검출 시스템)

  • Hwang, Sung-jin;Hong, Seok-woo;Yoon, Jong-seo;Park, Heemin;Kim, Hyun-chul
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.1
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    • pp.88-93
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    • 2021
  • The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.