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Design of robust deep learning-based object detection and classification model for autonomous driving applications

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Abstract

Recently, autonomous driving systems have become hot research which allows the drivers in making decisions to enhance safety, decrease traffic accidents, and move nearer toward completely autonomous cars and intelligent transportation systems. Autonomous driving systems necessitate consistent and accurate detection technique to detect objects in the real drivable environment. Though several object detection approaches have been available in the literature, a robust technique is needed for the recognition of occluded or truncated objects. Therefore, computer vision-based approaches can be used to accomplish cost-effective and robust solutions for the object detection process. In this aspect, this study focuses on the design of robust deep learning (DL)-enabled object detection and classification (RDL-ODC) model for autonomous driving systems. Primarily, preprocessing is performed to divide the images into local patches and transform them into a compatible form. In addition, the Adam optimizer-based MobileNetv2 model is applied as a feature extractor, and linear discriminant analysis (LDA) is used to reduce the dimensionality of the features. Moreover, the optimal kernel extreme learning machine (OKELM) model is employed as a classifier. To properly tune the parameters included in the KELM method, the cuckoo search optimization (CSO) algorithm is utilized, and consequently, the overall classification accuracy gets improvised, showing the novelty of the work. A wide variety of simulation takes place on benchmark dataset, and the results are investigated in terms of different evaluation metrics. The simulation result demonstrates the promising performances of the RDL-ODC technique over the advanced methods with the maximum average precision of 0.960 and minimum average miss rate of 0.192%.

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Data sharing not applicable to this article as no datasets were generated during the current study.

References

  • Arjmandi MK, Pooyan M, Mikaili M, Vali M, Moqarehzadeh A (2011) Identification of voice disorders using long-time features and support vector machine with different feature reduction methods. J Voice 25(6):e275–e289

    Article  Google Scholar 

  • Feng D, Harakeh A, Waslander SL, Dietmayer K (2021) A review and comparative study on probabilistic object detection in autonomous driving. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3096854

    Article  Google Scholar 

  • Fujiyoshi H, Hirakawa T, Yamashita T (2019) Deep learning-based image recognition for autonomous driving. IATSS Research 43(4):244–252

    Article  Google Scholar 

  • Hu K, Zhou Z, Weng L, Liu J, Wang L, Su Y, Yang Y (2017) An optimization strategy for weighted extreme learning machine based on PSO. Int J Pattern Recognit Artif Intell 31(01):1751001

    Article  Google Scholar 

  • https://www.kaggle.com/klemenko/kitti-dataset

  • Jiang X, Yu FR, Song T, Ma Z, Song Y, Zhu D (2020) Blockchain-enabled cross-domain object detection for autonomous driving: A model sharing approach. IEEE Internet Things J 7(5):3681–3692

    Article  Google Scholar 

  • John V, Mita S (2021) Deep feature-level sensor fusion using skip connections for real-time object detection in autonomous driving. Electronics 10(4):424

    Article  Google Scholar 

  • Liu Y, Yixuan Y, Liu M (2021) Ground-aware monocular 3d object detection for autonomous driving. IEEE Robot Autom Lett 6(2):919–926

    Article  Google Scholar 

  • Li P, Chen X, Shen S (2019) Stereo r-cnn based 3d object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7644–7652)

  • Li Y, Wang H, Dang LM, Nguyen TN, Han D, Lee A, Jang I, Moon H (2020) A deep learning-based hybrid framework for object detection and recognition in autonomous driving. IEEE Access 8:194228–194239

    Article  Google Scholar 

  • Mohapatra S, Yogamani S, Gotzig H, Milz S, Mader P (2021) BEVDetNet: bird's eye view LiDAR point cloud based real-time 3D object detection for autonomous driving. arXiv preprint arXiv: 2104.10780

  • Munir F, Azam S, Jeon M (2021) SSTN: self-supervised domain adaptation thermal object detection for autonomous driving. arXiv preprint arXiv:2103.03150

  • Nabati R, Qi H (2019) Rrpn: Radar region proposal network for object detection in autonomous vehicles. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 3093–3097). IEEE

  • Pham CC, Jeon JW (2017) Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks. Signal Process: Image Commun 53:110–122

    Google Scholar 

  • Qian R, Lai X, Li X (2021) 3D object detection for autonomous driving: a survey. arXiv preprint arXiv: 2106.10823.

  • Rashed H, Sallab AE, Yogamani S (2021) VM-MODNet: vehicle motion aware moving object detection for autonomous driving. arXiv preprint arXiv : 2104.10985

  • Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8):2852

    Article  Google Scholar 

  • Uçar A, Demir Y, Güzeliş C (2017) Object recognition and detection with deep learning for autonomous driving applications. Simulation 93(9):759–769

    Article  Google Scholar 

  • Wang G, Guo J, Chen Y, Li Y, Xu Q (2019) A PSO and BFO-based learning strategy applied to faster R-CNN for object detection in autonomous driving. IEEE Access 7:18840–18859

    Article  Google Scholar 

  • Wang J, Lan S, Gao M, Davis LS (2020) Infofocus: 3d object detection for autonomous driving with dynamic information modeling. In European Conference on Computer Vision (pp. 405–420). Springer, Cham

  • Wu B, Iandola F, Jin PH, Keutzer K (2017) Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 129–137)

  • Zhang Z (2018) Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1–2). IEEE

  • Zheng H, Zhou Y (2012) A novel cuckoo search optimization algorithm based on Gauss distribution. J Comput Inform Syst 8(10):4193–4200

    Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/209/42).

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Authors and Affiliations

  1. Department of Natural and Applied Sciences, College of Community - Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Mesfer Al Duhayyim

  2. Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia

    Fahd N. Al-Wesabi

  3. Faculty of Computer and IT, Sana’a University, Sana’s, Yemen

    Fahd N. Al-Wesabi

  4. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

    Anwer Mustafa Hilal & Manar Ahmed Hamza

  5. Department of Computer Science and Engineering, HMR Institute of Technology and Management, Hamidpur, Delhi, India

    Shalini Goel

  6. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta & Ashish Khanna

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  1. Mesfer Al Duhayyim
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  2. Fahd N. Al-Wesabi
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  3. Anwer Mustafa Hilal
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Correspondence to Anwer Mustafa Hilal.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by Irfan Uddin.

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Al Duhayyim, M., Al-Wesabi, F.N., Hilal, A.M. et al. Design of robust deep learning-based object detection and classification model for autonomous driving applications. Soft Comput 26, 7641–7652 (2022). https://doi.org/10.1007/s00500-021-06706-0

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