{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:21:52Z","timestamp":1775042512039,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centennial SIT Action for the 100th anniversary of Shibaura Institute of Technology entering the top 10 at the Asian Institute of Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer vision plays a significant role in mobile robot navigation due to the wealth of information extracted from digital images. Mobile robots localize and move to the intended destination based on the captured images. Due to the complexity of the environment, obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement. This study offers a real-time solution to the problem of extracting corridor scenes from a single image using a lightweight semantic segmentation model integrating with the quantization technique to reduce the numerous training parameters and computational costs. The proposed model consists of an FCN as the decoder and MobilenetV2 as the decoder (with multi-scale fusion). This combination allows us to significantly minimize computation time while achieving high precision. Moreover, in this study, we also propose to use the Balance Cross-Entropy loss function to handle diverse datasets, especially those with class imbalances and to integrate a number of techniques, for example, the Adam optimizer and Gaussian filters, to enhance segmentation performance. The results demonstrate that our model can outperform baselines across different datasets. Moreover, when being applied to practical experiments with a real mobile robot, the proposed model\u2019s performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.<\/jats:p>","DOI":"10.3390\/s23156907","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T11:23:03Z","timestamp":1691061783000},"page":"6907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1496-2492","authenticated-orcid":false,"given":"Thai-Viet","family":"Dang","sequence":"first","affiliation":[{"name":"Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7161-1077","authenticated-orcid":false,"given":"Dinh-Manh-Cuong","family":"Tran","sequence":"additional","affiliation":[{"name":"Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-0226","authenticated-orcid":false,"given":"Phan Xuan","family":"Tan","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu, Koto-ku, Tokyo 135-8548, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.3390\/app8040475","article-title":"Initial Results of Testing a Multilayer Laser Scanner in a Collision Avoidance System for Light Rail Vehicles","volume":"8","author":"Murat","year":"2018","journal-title":"Appl. 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