{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:56:41Z","timestamp":1770278201919,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Oceans and Fisheries"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m \u00d7 2 m grid within 10 min and a 2.5 m \u00d7 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.<\/jats:p>","DOI":"10.3390\/rs15143573","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T01:35:16Z","timestamp":1689644116000},"page":"3573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1729-4071","authenticated-orcid":false,"given":"Md. Al-Masrur","family":"Khan","sequence":"first","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, 37 Nakdong-Dearo 550 Beongil, Saha-gu, Busan 49315, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1237-0845","authenticated-orcid":false,"given":"Regidestyoko Wasistha","family":"Harseno","sequence":"additional","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, 37 Nakdong-Dearo 550 Beongil, Saha-gu, Busan 49315, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7743-4881","authenticated-orcid":false,"given":"Seong-Hoon","family":"Kee","sequence":"additional","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, 37 Nakdong-Dearo 550 Beongil, Saha-gu, Busan 49315, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2391-5767","authenticated-orcid":false,"given":"Abdullah-Al","family":"Nahid","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","unstructured":"Lee, R.B. 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