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    Volume 2, Issue 1, IECE Transactions on Intelligent Systematics
    Volume 2, Issue 1, 2024
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    Quanmin Zhu
    Quanmin Zhu
    University of the West of England, United Kingdom
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    IECE Transactions on Intelligent Systematics, 2024, Volume 2, Issue 1: 14-26

    Free Access | Research Article | 27 December 2024
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    by
    IECE Author Fida Hussain Dahri 1
    IECE Author Ghulam E Mustafa Abro 2 *
    IECE Author Nisar Ahmed Dahri 3
    IECE Author Asif Ali Laghari 4
    IECE Author Zain Anwar Ali 5
    1 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
    2 Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Kingdom of Saudi Arabia
    3 Faculty of Social Sciences and Humanities, School of Education, University Technology Malaysia, Malaysia
    4 Software College, Shenyang Normal University, Shenyang 110136, China
    5 Electronic Engineering Department, Maynooth International Engineering College (MIEC), Maynooth University, Maynooth, Co. Kildare, Ireland
    * Corresponding Author: Ghulam E Mustafa Abro, [email protected]
    DOI: 10.62762/TIS.2025.613103
    Received: 17 September 2024, Accepted: 09 December 2024, Published: 27 December 2024  
    Abstract
    Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a training accuracy of 99%, a testing accuracy of 89.73%, a precision of 90.11%, a recall of 89.73%, and an F1-score of 89.79%. These findings underscore the efficacy of our methodology in tackling the intricacies of indoor scene recognition. This research substantially advances the domain of robotics and automation by establishing a more resilient and dependable framework for autonomous navigation and scene comprehension in GPS-denied settings, facilitating the development of more efficient and intelligent robotic systems.
    Graphical Abstract
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    Keywords
    indoor scene recognition
    deep convolutional neural network (CNN)
    robotics and automation autonomous navigation and GPS-Denied environments
    Funding
    This work was jointly supported by the Data and Intelligence Laboratory (D&Intel Lab), School of Computer Science and Engineering, Southeast University, China and the Robotics Control lab under the Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), Kingdom of Saudi Arabia.
    References
    1. Macrorie, R., Marvin, S., & While, A. (2021). Robotics and automation in the city: a research agenda. Urban Geography, 42(2), 197-217.
      [Google Scholar]
    2. Kolpashchikov, D., Gerget, O., & Meshcheryakov, R. (2022). Robotics in healthcare. Handbook of Artificial Intelligence in Healthcare: Vol 2: Practicalities and Prospects, 281-306.
      [Google Scholar]
    3. Afif, M., Ayachi, R., Said, Y., & Atri, M. (2022). An evaluation of EfficientDet for object detection used for indoor robots assistance navigation. Journal of Real-Time Image Processing, 19(3), 651-661.
      [Google Scholar]
    4. Heikel, E., & Espinosa-Leal, L. (2022). Indoor scene recognition via object detection and TF-IDF. Journal of Imaging, 8(8), 209.
      [Google Scholar]
    5. Glavan, A., & Talavera, E. (2022). InstaIndoor and multi-modal deep learning for indoor scene recognition. Neural Computing and Applications, 34(9), 6861-6877.
      [Google Scholar]
    6. Fang, W., Chen, L., Zhang, T., Chen, C., Teng, Z., & Wang, L. (2023). Head-mounted display augmented reality in manufacturing: A systematic review. Robotics and Computer-Integrated Manufacturing, 83, 102567.
      [Google Scholar]
    7. Khan, S. H., Hayat, M., Bennamoun, M., Togneri, R., & Sohel, F. A. (2016). A discriminative representation of convolutional features for indoor scene recognition. IEEE Transactions on Image Processing, 25(7), 3372-3383.
      [Google Scholar]
    8. Khan, S. D., & Othman, K. M. (2024). Indoor Scene Classification through Dual-Stream Deep Learning: A Framework for Improved Scene Understanding in Robotics. Computers, 13(5), 121.
      [Google Scholar]
    9. Li, X. (2024, April). Intelligent Inspection Robot Scene Recognition under Convolutional Neural Network. In 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 519-524). IEEE.
      [Google Scholar]
    10. Santos, D., Lopez-Lopez, E., Pardo, X. M., Iglesias, R., Barro, S., & Fdez-Vidal, X. R. (2019). Robust and fast scene recognition in robotics through the automatic identification of meaningful images. Sensors, 19(18), 4024.
      [Google Scholar]
    11. Dahri, F. H., Dahri, N. A., & Soomro, M. A. (2023). Image caption generator using convolutional recurrent neural network feature fusion. Journal of Xi’an Shiyou University, Natural Science Edition, 9, 1088-1095.
      [Google Scholar]
    12. Sharma, V., Nagpal, N., Shandilya, A., Dureja, A., & Dureja, A. (2022, December). A Practical Approach to detect Indoor and Outdoor Scene Recognition. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (pp. 1-10).
      [Google Scholar]
    13. Zhu, Y., Luo, H., Zhao, F., & Chen, R. (2020). Indoor/outdoor switching detection using multisensor DenseNet and LSTM. IEEE Internet of Things Journal, 8(3), 1544-1556.
      [Google Scholar]
    14. Kuriakose, B., Shrestha, R., & Sandnes, F. E. (2021, October). SceneRecog: a deep learning scene recognition model for assisting blind and visually impaired navigate using smartphones. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2464-2470). IEEE.
      [Google Scholar]
    15. Alqobali, R., Alshmrani, M., Alnasser, R., Rashidi, A., Alhmiedat, T., & Alia, O. M. D. (2023). A survey on robot semantic navigation systems for indoor environments. Applied Sciences, 14(1), 89.
      [Google Scholar]
    16. Wijayathunga, L., Rassau, A., & Chai, D. (2023). Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review. Applied Sciences, 13(17), 9877.
      [Google Scholar]
    17. Daou, A., Pothin, J. B., Honeine, P., & Bensrhair, A. (2023). Indoor scene recognition mechanism based on direction-driven convolutional neural networks. Sensors, 23(12), 5672.
      [Google Scholar]
    18. Kumar, N., Singh, H., Varshney, M. T., Malik, M. V., & Kumar, V. (2022). Indoor and Outdoor Scene Recognition. Grenze International Journal of Engineering & Technology (GIJET), 8(2).
      [Google Scholar]
    19. Georgiou, A., Masters, P., Johnson, S., & Feetham, L. (2022). UAV-assisted real-time evidence detection in outdoor crime scene investigations. Journal of forensic sciences, 67(3), 1221-1232.
      [Google Scholar]
    20. Liu, S., & Tian, G. (2019). An indoor scene classification method for service robot Based on CNN feature. Journal of Robotics, 2019(1), 8591035.
      [Google Scholar]
    21. Rafique, A. A., Gochoo, M., Jalal, A., & Kim, K. (2023). Maximum entropy scaled super pixels segmentation for multi-object detection and scene recognition via deep belief network. Multimedia Tools and Applications, 82(9), 13401-13430.
      [Google Scholar]
    22. Zhao, X., & Cheah, C. C. (2023). BIM-based indoor mobile robot initialization for construction automation using object detection. Automation in Construction, 146, 104647.
      [Google Scholar]
    23. Wang, H., & Li, M. (2024). A new era of indoor scene reconstruction: A survey. IEEE Access, 12, 110160-110192.
      [Google Scholar]
    24. Choe, S., Seong, H., & Kim, E. (2021). Indoor place category recognition for a cleaning robot by fusing a probabilistic approach and deep learning. IEEE Transactions on Cybernetics, 52(8), 7265-7276.
      [Google Scholar]
    25. Cheng, C., Koschan, A., Chen, C. H., Page, D. L., & Abidi, M. A. (2011). Outdoor scene image segmentation based on background recognition and perceptual organization. IEEE transactions on image processing, 21(3), 1007-1019.
      [Google Scholar]
    26. Gupta, S., Arbelaez, P., & Malik, J. (2013). Perceptual organization and recognition of indoor scenes from RGB-D images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 564-571).
      [Google Scholar]
    27. Zhou, Z., Li, L., Fürsterling, A., Durocher, H. J., Mouridsen, J., & Zhang, X. (2022). Learning-based object detection and localization for a mobile robot manipulator in SME production. Robotics and Computer-Integrated Manufacturing, 73, 102229.
      [Google Scholar]
    28. Samani, E. U., Yang, X., & Banerjee, A. G. (2021). Visual object recognition in indoor environments using topologically persistent features. IEEE Robotics and Automation Letters, 6(4), 7509-7516.
      [Google Scholar]
    29. Silvera-Tawil, D. (2024). Robotics in Healthcare: A Survey. SN Computer Science, 5(1), 189.
      [Google Scholar]
    30. Liu, M., Chen, M., Wu, Z., Zhong, B., & Deng, W. (2024). Implementation of Intelligent Indoor Service Robot Based on ROS and Deep Learning. Machines, 12(4), 256.
      [Google Scholar]
    31. Strader, J., Hughes, N., Chen, W., Speranzon, A., & Carlone, L. (2024). Indoor and outdoor 3d scene graph generation via language-enabled spatial ontologies. IEEE Robotics and Automation Letters, 9(6), 4886-4893.
      [Google Scholar]
    32. Liu, Z., Wang, J., Li, J., Liu, P., & Ren, K. (2023). A novel multiple targets detection method for service robots in the indoor complex scenes. Intelligent Service Robotics, 16(4), 453-469.
      [Google Scholar]
    33. Von Itzstein, G. S., Billinghurst, M., Smith, R. T., & Thomas, B. H. (2024). Augmented reality entertainment: Taking gaming out of the box. In Encyclopedia of computer graphics and games (pp. 162-170). Cham: Springer International Publishing.
      [Google Scholar]
    34. Feng, J., Sun, J., & Yao, Y. (2023, April). Design of Intelligent Service Robot for Military Recuperation. In 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT) (pp. 131-137). IEEE.
      [Google Scholar]
    35. Heckelman, L. N., Soher, B. J., Spritzer, C. E., Lewis, B. D., & DeFrate, L. E. (2022). Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency. Scientific Reports, 12(1), 7825.
      [Google Scholar]
    36. Borrego, A., Latorre, J., Alcañiz, M., & Llorens, R. (2018). Comparison of Oculus Rift and HTC Vive: feasibility for virtual reality-based exploration, navigation, exergaming, and rehabilitation. Games for health journal, 7(3), 151-156.
      [Google Scholar]
    37. Maruhn, P. (2021). VR Pedestrian Simulator Studies at Home: Comparing Google Cardboards to Simulators in the Lab and Reality. Frontiers in Virtual Reality, 2, 746971.
      [Google Scholar]
    38. Lee, C. D. (2021). A Review of Virtual and Augmented Reality Concepts, Technologies and Application. Journal of Computing and Natural Science, 1(4), 139-144.
      [Google Scholar]
    39. Qi, J., Ma, L., Cui, Z., & Yu, Y. (2024). Computer vision-based hand gesture recognition for human-robot interaction: a review. Complex & Intelligent Systems, 10(1), 1581-1606.
      [Google Scholar]
    40. Bhola, G., & Vishwakarma, D. K. (2024). A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects. Multimedia Tools and Applications, 83(1), 1965-2005.
      [Google Scholar]
    41. Emek Soylu, B., Guzel, M. S., Bostanci, G. E., Ekinci, F., Asuroglu, T., & Acici, K. (2023). Deep-learning-based approaches for semantic segmentation of natural scene images: A review. Electronics, 12(12), 2730.
      [Google Scholar]
    42. Ismail, A. S., Seifelnasr, M. M., & Guo, H. (2018, April). Understanding indoor scene: Spatial layout estimation, scene classification, and object detection. In Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing (pp. 64-70).
      [Google Scholar]
    43. Sitaula, C., Xiang, Y., Zhang, Y., Lu, X., & Aryal, S. (2019). Indoor image representation by high-level semantic features. IEEE Access, 7, 84967-84979.
      [Google Scholar]
    44. Susan, S., & Tuteja, M. (2024). Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey. International Journal of Image and Graphics, 2550054.
      [Google Scholar]
    45. Guo, J., Chen, H., Liu, B., & Xu, F. (2023). A system and method for person identification and positioning incorporating object edge detection and scale-invariant feature transformation. Measurement, 223, 113759.
      [Google Scholar]
    46. Afif, M., Ayachi, R., Said, Y., & Atri, M. (2020). Deep learning based application for indoor scene recognition. Neural Processing Letters, 51, 2827-2837.
      [Google Scholar]
    47. Surendran, R., Chihi, I., Anitha, J., & Hemanth, D. J. (2023). Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine. Algorithms, 16(9), 430.
      [Google Scholar]
    48. Singh, A., Pandey, P., Puig, D., Nandi, G. C., & Abdel-Nasser, M. (2022). Reliable Scene Recognition Approach for Mobile Robots with Limited Resources Based on Deep Learning and Neuro-Fuzzy Inference. Traitement du Signal, 39(4), 1255.
      [Google Scholar]
    49. Quattoni, A., & Torralba, A. (2009, June). Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition (pp. 413-420). IEEE.
      [Google Scholar]
    50. Bose, D., Hebbar, R., Somandepalli, K., Zhang, H., Cui, Y., Cole-McLaughlin, K., ... & Narayanan, S. (2023). Movieclip: Visual scene recognition in movies. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2083-2092).
      [Google Scholar]
    51. Velikov, K. (2023). Enhancing Semantic Segmentation for Indoor Environments: Integrating Depth Information into Neural Networks (Bachelor’s thesis, University of Twente).
      [Google Scholar]
    52. Piekenbrinck, J., Hermans, A., Vaskevicius, N., Linder, T., & Leibe, B. (2024). RGB-D Cube R-CNN: 3D Object Detection with Selective Modality Dropout. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1997-2006).
      [Google Scholar]
    53. Naidu, G., Zuva, T., & Sibanda, E. M. (2023, April). A review of evaluation metrics in machine learning algorithms. In Computer Science On-line Conference (pp. 15-25). Cham: Springer International Publishing.
      [Google Scholar]
    54. Javed, M., Zhang, Z., Dahri, F. H., & Laghari, A. A. (2024). Real-time deepfake video detection using eye movement analysis with a hybrid deep learning approach. Electronics, 13(15), 2947.
      [Google Scholar]
    55. Miao, B., Zhou, L., Mian, A. S., Lam, T. L., & Xu, Y. (2021, September). Object-to-scene: Learning to transfer object knowledge to indoor scene recognition. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2069-2075). IEEE.
      [Google Scholar]
    56. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6), 1452-1464.
      [Google Scholar]
    57. Zhou, Z., Zhang, J., Gong, C., & Wu, W. (2023). Automatic tunnel lining crack detection via deep learning with generative adversarial network-based data augmentation. Underground Space, 9, 140-154.
      [Google Scholar]
    58. Xie, L., Lee, F., Liu, L., Kotani, K., & Chen, Q. (2020). Scene recognition: A comprehensive survey. Pattern Recognition, 102, 107205.
      [Google Scholar]
    59. Anbarasu, B., & Anitha, G. (2018). Indoor scene recognition for micro aerial vehicles navigation using enhanced-GIST descriptors. Defence Science Journal, 68(2), 129.
      [Google Scholar]
    60. Yue, H., Lehtola, V., Wu, H., Vosselman, G., Li, J., & Liu, C. (2024). Recognition of Indoor Scenes using 3D Scene Graphs. IEEE Transactions on Geoscience and Remote Sensing.
      [Google Scholar]
    61. Chen, H., Li, Y., & Su, D. (2019). Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection. Pattern Recognition, 86, 376-385.
      [Google Scholar]
    62. Kim, S. J., & Shin, D. H. (2017, January). The effects of ambient scent on hedonic experience on online shopping. In Proceedings of the 11th international conference on ubiquitous information management and communication (pp. 1-5).
      [Google Scholar]
    63. Dahri, F. H. (2022). Automatic Face Mask Detection and Recognition Using Deep Learning. ScienceOpen Preprints, 13(11), 433-447.
      [Google Scholar]
    Cite This Article
    APA Style
    Dahri, F. H., Abro, G. E. M., Dahri, N. A., Laghari, A. A., & Ali, Z. A. (2024). Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition. IECE Transactions on Intelligent Systematics, 2(1), 14–26. https://doi.org/10.62762/TIS.2025.613103
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