Enhanced Indoor Scene Classification Using YOLO v11 and RCNN: A Deep Learning Perspective

DOI

10.2991/978-94-6239-768-2_18How to use a DOI?

Keywords

Indoor Scene Classification; Hybrid Model; RCNN YOLO v11; Object Detection real-time processing; Dynamic Object Tracking; Semantic Elements Benchmark Evaluation

Abstract

Indoor scene classification concerns a paramount task in computer vision: categorizing an indoor environment like a kitchen or office into predefined classes. In its application, this paper uses a mixed model of RCNN and YOLOv11 to address its greatest challenges: complex layouts and diversity in lighting and objects. We have introduced a hybrid Dee learning model, which breaks scenes into smaller parts to ensure related semantic elements can be distinguished well for object detection and classification. The hybrid model combines the real-time detection feature of YOLOv11 with the precision of RCNN to improve system performance. It is optimized using tools such as OpenCV, TensorFlow, and Kera’s to be used in real-time applications, including object tracking, dynamic object monitoring, and security enhancement. Benchmark evaluations show large improvements in terms of accuracy, processing speed, and robustness compared to the traditional methods.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Showkat A. Dar
AU  - Tawseef A. Mir
AU  - K. Harshitha
AU  - V. Likhitha
AU  - A. Vijaya Mahendra Varman
AU  - P. Rekha
PY  - 2026
DA  - 2026/09/07
TI  - Enhanced Indoor Scene Classification Using YOLO v11 and RCNN: A Deep Learning Perspective
BT  - Proceedings of the Third International Conference on Recent Advances in Computing Sciences (RACS 2025)
PB  - Atlantis Press
SP  - 171
EP  - 179
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-768-2_18
DO  - 10.2991/978-94-6239-768-2_18
ID  - Dar2026
ER  -