An automatic pothole detection algorithm using pavement 3D data
Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and tim...
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| Vydáno v: | The international journal of pavement engineering Ročník 24; číslo 2 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Abingdon
Taylor & Francis
28.01.2023
Taylor & Francis LLC |
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| ISSN: | 1029-8436, 1477-268X |
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| Abstract | Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and times of surveys, several innovative methods to detect, classify and measure surface distresses were proposed, with variable results. In this context, the authors propose an algorithm for automated pothole detection through the processing of 3D data of pavement surfaces, acquired using an innovative high-performance equipment. The algorithm, derived from computer vision, is able of identifying potholes in road sections, assuring a reliable estimation of shape and severity, in terms not only of area, perimeter, but also depth, with practical benefits. The numerical results show the remarkable performance of the proposed algorithm, even compared to alternative traditional methodologies. In terms of Precision, Recall and F-Score, it assures mean values equal respectively to 89.75%, 92.95% 91.28%. Validation was also performed in terms of area error rate, with an average value of 5.15%, significantly lower than other approaches. Then, the algorithm represents a reliable alternative to traditional approaches and allows road administrators to derive data to optimize maintenance and road functionality. |
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| AbstractList | Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and times of surveys, several innovative methods to detect, classify and measure surface distresses were proposed, with variable results. In this context, the authors propose an algorithm for automated pothole detection through the processing of 3D data of pavement surfaces, acquired using an innovative high-performance equipment. The algorithm, derived from computer vision, is able of identifying potholes in road sections, assuring a reliable estimation of shape and severity, in terms not only of area, perimeter, but also depth, with practical benefits. The numerical results show the remarkable performance of the proposed algorithm, even compared to alternative traditional methodologies. In terms of Precision, Recall and F-Score, it assures mean values equal respectively to 89.75%, 92.95% 91.28%. Validation was also performed in terms of area error rate, with an average value of 5.15%, significantly lower than other approaches. Then, the algorithm represents a reliable alternative to traditional approaches and allows road administrators to derive data to optimize maintenance and road functionality. |
| Author | Modica, M. Pellegrino, O. Sollazzo, G. Bosurgi, G. |
| Author_xml | – sequence: 1 givenname: G. orcidid: 0000-0002-0782-5510 surname: Bosurgi fullname: Bosurgi, G. organization: University of Messina – sequence: 2 givenname: M. surname: Modica fullname: Modica, M. organization: University of Messina – sequence: 3 givenname: O. orcidid: 0000-0002-7990-3581 surname: Pellegrino fullname: Pellegrino, O. organization: University of Messina – sequence: 4 givenname: G. orcidid: 0000-0002-7116-7946 surname: Sollazzo fullname: Sollazzo, G. email: gsollazzo@unime.it organization: University of Messina |
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| References | e_1_3_3_30_1 Kim T. (e_1_3_3_17_1) 2014; 5 e_1_3_3_18_1 e_1_3_3_19_1 e_1_3_3_14_1 e_1_3_3_37_1 e_1_3_3_13_1 e_1_3_3_38_1 e_1_3_3_16_1 e_1_3_3_35_1 e_1_3_3_15_1 Salem S. A. (e_1_3_3_27_1) 2010; 2 e_1_3_3_36_1 Bosurgi G. (e_1_3_3_4_1) 2019 e_1_3_3_10_1 e_1_3_3_33_1 e_1_3_3_34_1 e_1_3_3_12_1 e_1_3_3_31_1 e_1_3_3_11_1 e_1_3_3_32_1 e_1_3_3_7_1 e_1_3_3_6_1 e_1_3_3_9_1 e_1_3_3_8_1 e_1_3_3_29_1 e_1_3_3_28_1 e_1_3_3_25_1 e_1_3_3_24_1 e_1_3_3_26_1 e_1_3_3_3_1 e_1_3_3_21_1 e_1_3_3_2_1 e_1_3_3_20_1 e_1_3_3_5_1 e_1_3_3_23_1 e_1_3_3_22_1 |
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| SubjectTerms | 3D data Algorithms automatic distress detection Computer vision Data acquisition Pavement potholes pavement quality Pavements Quality assurance Road maintenance |
| Title | An automatic pothole detection algorithm using pavement 3D data |
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