{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T07:05:09Z","timestamp":1775718309199,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agency for Science, Technology and Research, National Robotics Programme under its Robotics Enabling Capabilities and Technologies","award":["192 25 00051 and 192 22 00108"],"award-info":[{"award-number":["192 25 00051 and 192 22 00108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot\u2019s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.<\/jats:p>","DOI":"10.3390\/s21217287","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4085-5698","authenticated-orcid":false,"given":"Povendhan","family":"Palanisamy","sequence":"first","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6504-1530","authenticated-orcid":false,"given":"Rajesh Elara","family":"Mohan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7096-8039","authenticated-orcid":false,"given":"Archana","family":"Semwal","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Lee Ming","family":"Jun Melivin","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Braulio","family":"F\u00e9lix G\u00f3mez","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Selvasundari","family":"Balakrishnan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-5495","authenticated-orcid":false,"given":"Karthikeyan","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-9814","authenticated-orcid":false,"given":"Balakrishnan","family":"Ramalingam","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Dylan Ng","family":"Terntzer","sequence":"additional","affiliation":[{"name":"LionsBot International Pte. Ltd., #03-02, 11 Changi South Street 3, Singapore 486122, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"ref_1","first-page":"615","article-title":"Designing Smart Sewerbot for the Identification of Sewer Defects and Blockages","volume":"10","author":"Abro","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_2","unstructured":"(2021, November 01). Crawler Camera System Market Size Report, 2020\u20132027. Market Analysis Report. Available online: https:\/\/www.grandviewresearch.com\/industry-analysis\/crawler-camera-system-market."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tennakoon, R.B., Hoseinnezhad, R., Tran, H., and Bab-Hadiashar, A. (2018). Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks. 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