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IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation.

التفاصيل البيبلوغرافية
العنوان: IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation.
المؤلفون: Dang TV; Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam., Tran DM; Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 10000, Vietnam., Tan PX; Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu, Koto-ku, Tokyo 135-8548, Japan.
المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Aug 03; Vol. 23 (15). Date of Electronic Publication: 2023 Aug 03.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI, c2000-
مستخلص: 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's performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.
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فهرسة مساهمة: Keywords: computer vision; mobile robot; navigation; obstacle avoidance; semantic segmentation
تواريخ الأحداث: Date Created: 20230812 Latest Revision: 20230815
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC10422405
DOI: 10.3390/s23156907
PMID: 37571691
قاعدة البيانات: MEDLINE
الوصف
تدمد:1424-8220
DOI:10.3390/s23156907