Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm
•Asphalt pavement thickness is detected by GPR system for its advantage of non-destructive.•The traditional Canny algorithm, the connected region detection algorithm and the improved Canny algorithm were compared by simulated and actual images.•The improved Canny algorithm was used to detect GPR ima...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2022-06, Vol.196, p.111248, Article 111248 |
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| container_title | Measurement : journal of the International Measurement Confederation |
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| creator | Wang, Lutai Gu, Xingyu Liu, Zhen Wu, Wenxiu Wang, Danyu |
| description | •Asphalt pavement thickness is detected by GPR system for its advantage of non-destructive.•The traditional Canny algorithm, the connected region detection algorithm and the improved Canny algorithm were compared by simulated and actual images.•The improved Canny algorithm was used to detect GPR images, which has a smaller error compared with the traditional Canny algorithm and the connected region detection algorithm.
The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional Canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved Canny algorithm was compared with the traditional Canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved Canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness. |
| doi_str_mv | 10.1016/j.measurement.2022.111248 |
| format | Article |
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The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional Canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved Canny algorithm was compared with the traditional Canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved Canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness.</description><identifier>ISSN: 0263-2241</identifier><identifier>EISSN: 1873-412X</identifier><identifier>DOI: 10.1016/j.measurement.2022.111248</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Algorithms ; Asphalt ; Asphalt pavements ; Core sampling ; Coring ; Digital Image Processing ; GprMax ; Ground Penetrating Radar ; Improved Canny Algorithm ; Non-destructive Detecting ; Pavement Thickness ; Search algorithms ; Swarm intelligence ; Thickness ; Thickness measurement</subject><ispartof>Measurement : journal of the International Measurement Confederation, 2022-06, Vol.196, p.111248, Article 111248</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Jun 15, 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-1626cb55d6dfae3c422fa9da78155c03712eed9d8dc5bc35aa280944e34710123</citedby><cites>FETCH-LOGICAL-c349t-1626cb55d6dfae3c422fa9da78155c03712eed9d8dc5bc35aa280944e34710123</cites><orcidid>0000-0001-8442-8271 ; 0000-0002-8561-7223</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0263224122004948$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Wang, Lutai</creatorcontrib><creatorcontrib>Gu, Xingyu</creatorcontrib><creatorcontrib>Liu, Zhen</creatorcontrib><creatorcontrib>Wu, Wenxiu</creatorcontrib><creatorcontrib>Wang, Danyu</creatorcontrib><title>Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm</title><title>Measurement : journal of the International Measurement Confederation</title><description>•Asphalt pavement thickness is detected by GPR system for its advantage of non-destructive.•The traditional Canny algorithm, the connected region detection algorithm and the improved Canny algorithm were compared by simulated and actual images.•The improved Canny algorithm was used to detect GPR images, which has a smaller error compared with the traditional Canny algorithm and the connected region detection algorithm.
The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional Canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved Canny algorithm was compared with the traditional Canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved Canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness.</description><subject>Algorithms</subject><subject>Asphalt</subject><subject>Asphalt pavements</subject><subject>Core sampling</subject><subject>Coring</subject><subject>Digital Image Processing</subject><subject>GprMax</subject><subject>Ground Penetrating Radar</subject><subject>Improved Canny Algorithm</subject><subject>Non-destructive Detecting</subject><subject>Pavement Thickness</subject><subject>Search algorithms</subject><subject>Swarm intelligence</subject><subject>Thickness</subject><subject>Thickness measurement</subject><issn>0263-2241</issn><issn>1873-412X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqNkE9PAjEQxRujiYh-hxrPi_2zu2y9EaJoQqIxmnhrSjsLXdkW20LCt7eIB4-eZg7vvZn3Q-iakhEltL7tRj2ouA3Qg0sjRhgbUUpZ2ZygAW3GvCgp-zhFA8JqXjBW0nN0EWNHCKm5qAeon2yT71WyGhtIoJP1DvsWq7hZqXXCG7X7icZpZfWngxjv8AT3kFbeYO37hXXWLfHs5RXbXi0hYuVMXjfB78DgqXJuj9V66YNNq_4SnbVqHeHqdw7R-8P92_SxmD_PnqaTeaF5KVJBa1brRVWZ2rQKuC4Za5UwatzQqtKEjykDMMI0RlcLzSulWENEWQIvx5kK40N0c8zNb3xtISbZ-W1w-aRkddNUohGVyCpxVOngYwzQyk3IJcJeUiIPdGUn_9CVB7rySDd7p0cv5Bo7C0FGbcFpMDZkitJ4-4-UbyBciqE</recordid><startdate>20220615</startdate><enddate>20220615</enddate><creator>Wang, Lutai</creator><creator>Gu, Xingyu</creator><creator>Liu, Zhen</creator><creator>Wu, Wenxiu</creator><creator>Wang, Danyu</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-8442-8271</orcidid><orcidid>https://orcid.org/0000-0002-8561-7223</orcidid></search><sort><creationdate>20220615</creationdate><title>Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm</title><author>Wang, Lutai ; Gu, Xingyu ; Liu, Zhen ; Wu, Wenxiu ; Wang, Danyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-1626cb55d6dfae3c422fa9da78155c03712eed9d8dc5bc35aa280944e34710123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Asphalt</topic><topic>Asphalt pavements</topic><topic>Core sampling</topic><topic>Coring</topic><topic>Digital Image Processing</topic><topic>GprMax</topic><topic>Ground Penetrating Radar</topic><topic>Improved Canny Algorithm</topic><topic>Non-destructive Detecting</topic><topic>Pavement Thickness</topic><topic>Search algorithms</topic><topic>Swarm intelligence</topic><topic>Thickness</topic><topic>Thickness measurement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Lutai</creatorcontrib><creatorcontrib>Gu, Xingyu</creatorcontrib><creatorcontrib>Liu, Zhen</creatorcontrib><creatorcontrib>Wu, Wenxiu</creatorcontrib><creatorcontrib>Wang, Danyu</creatorcontrib><collection>CrossRef</collection><jtitle>Measurement : journal of the International Measurement Confederation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Lutai</au><au>Gu, Xingyu</au><au>Liu, Zhen</au><au>Wu, Wenxiu</au><au>Wang, Danyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm</atitle><jtitle>Measurement : journal of the International Measurement Confederation</jtitle><date>2022-06-15</date><risdate>2022</risdate><volume>196</volume><spage>111248</spage><pages>111248-</pages><artnum>111248</artnum><issn>0263-2241</issn><eissn>1873-412X</eissn><abstract>•Asphalt pavement thickness is detected by GPR system for its advantage of non-destructive.•The traditional Canny algorithm, the connected region detection algorithm and the improved Canny algorithm were compared by simulated and actual images.•The improved Canny algorithm was used to detect GPR images, which has a smaller error compared with the traditional Canny algorithm and the connected region detection algorithm.
The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional Canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved Canny algorithm was compared with the traditional Canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved Canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness.</abstract><cop>London</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.measurement.2022.111248</doi><orcidid>https://orcid.org/0000-0001-8442-8271</orcidid><orcidid>https://orcid.org/0000-0002-8561-7223</orcidid></addata></record> |
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| ispartof | Measurement : journal of the International Measurement Confederation, 2022-06, Vol.196, p.111248, Article 111248 |
| issn | 0263-2241 1873-412X |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Algorithms Asphalt Asphalt pavements Core sampling Coring Digital Image Processing GprMax Ground Penetrating Radar Improved Canny Algorithm Non-destructive Detecting Pavement Thickness Search algorithms Swarm intelligence Thickness Thickness measurement |
| title | Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm |
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