Evaluation of image quality based on visual perception using antagonistic networks in autonomous vehicles

T. Sudarson Rama Perumal; Vartika Kulshrestha; Rajveer K. Shastri; J. Immanuel Durai Raj; S. Balu Mahandiran; C. M. Velu

doi:10.53730/ijhs.v6nS3.8731

Authors

T. Sudarson Rama Perumal Associate Professor, Department of Electronics and communications engineering, Rohini college of engineering and technology, India
Vartika Kulshrestha Assistant Professor, Department of Computer Science & Engineering, Alliance University, Bangalore, Karnataka, India
Rajveer K. Shastri Professor, Department of E&TC, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, India
J. Immanuel Durai Raj Assistant Professor, Department of Mechanical Engineering, St. Joseph's Institute of Technology, Chennai, India
S. Balu Mahandiran Engineering and Technology, Kuniyamuthur, Coimbatore, India
C. M. Velu Professor, Department of Computer Science Engineering, Saveetha School of Engg, SIMATS, Saveetha University, Chennai

Keywords:

deep learning, automated vehicles, embedded systems, machine learning, sensors

Abstract

A method for just a point-to-point deep learning model for automated vehicles is described in this research. Our major goal was to develop an automated vehicle using a lightweight deep learning model that could be deployed on integrated modern vehicles. There is various point to point deep learning model used for automated vehicles, with camera pictures as input to the machine learning techniques and guiding direction projection as output, however, these deep learning methods are substantially more sophisticated than the cloud infrastructure we suggest. The proposed program's infrastructure, high computational, and summative assessment while automated vehicles are compared with different previous machine learning algorithms that We actually through order to achieve our goal, an accurate assessment. The proposed program's predictive model is 4 sets lower than PilotNet's and around 250 times less than AlexNet's. Although the innovative platform's intricacy and size are decreased in contrast to all other designs, resulting in reduced delay and greater refresh rate throughout reasoning, the model preserved its efficiency, accomplishing successful automated vehicles at the comparable economy as two additional designs. Furthermore, the proposed deep learning model decreased the processing capability, price, and storage requirements for true interpretation devices.

Downloads

Download data is not yet available.

References

Kocić J, Jovičić N, Drndarević V. Sensors and sensor fusion in autonomous vehicles. In2018 26th Telecommunications Forum (TELFOR) 2018 Nov 20 (pp. 420-425). IEEE.

Bresson G, Alsayed Z, Yu L, Glaser S. Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles. 2017 Sep 4;2(3):194-220.

Badue C, Guidolini R, Carneiro RV, Azevedo P, Cardoso VB, Forechi A, Jesus L, Berriel R, Paixao TM, Mutz F, de Paula Veronese L. Self-driving cars: A survey. Expert Systems with Applications. 2021 Mar 1;165:113816.

Jha S, Raman V. Automated synthesis of safe autonomous vehicle control under perception uncertainty. NASA Formal Methods Symposium 2016 Jun 7 (pp. 117-132). Springer, Cham.

Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. Journal of Field Robotics. 2020 Apr;37(3):362-86.

Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP. Stock price prediction using LSTM, RNN, and CNN-sliding window model. In2017 international conference on advances in computing, communications, and informatics (icacci) 2017 Sep 13 (pp. 1643-1647). IEEE.

Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 2020 Dec;53(8):5455-516.

Avanzato R, Beritelli F, Di Franco F, Puglisi VF. A convolutional neural networks approach to audio classification for rainfall estimation. In2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2019 Sep 18 (Vol. 1, pp. 285-289). IEEE.

Avanzato R, Beritelli F. Automatic ECG diagnosis using convolutional neural network. Electronics. 2020 Jun;9(6):951.

Wang K, Qi X, Liu H. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied Energy. 2019 Oct 1;251:113315.

Wang D, Zhang M, Li Z, Li J, Song C, Li J, Wang M. Convolutional neural network-based deep learning for intelligent OSNR estimation on eye diagrams. In2017 European Conference on Optical Communication (ECOC) 2017 Sep 17 (pp. 1-3). IEEE.

Wu Q, Han B, Li G, Shahidehpour M. Power Flow Jacobian Matrix-based Bidirectional Voltage Stability Evaluation with deep PV Penetration by CNN. In2019 IEEE Power & Energy Society General Meeting (PESGM) 2019 Aug 4 (pp. 1-5). IEEE.

Bazai H, Kargar E, Mehrabi M. Using an encoder-decoder convolutional neural network to predict the solid holdup patterns in a pseudo-2d fluidized bed. Chemical Engineering Science. 2021 Dec 31;246:116886.

Liu M, Jervis M, Li W, Nivlet P. Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks. Geophysics. 2020 Jul 1;85(4): O47-58.

Utilizing scratch to create computational thinking at school with artificial intelligence Kumari, M.K., Latchoumi, T.P., Kalusuraman, G., Chithambarathanu, M., Parthiban, L.A Closer Look at Big Data Analytics, 2021, pp. 163–193

Latchoumi, T. P., Kalusuraman, G., Banu, J. F., Yookesh, T. L., Ezhilarasi, T. P., & Balamurugan, K. (2021, November). Enhancement in manufacturing systems using Grey-Fuzzy and LK-SVM approach. In 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) (pp. 72-78). IEEE.

Karnan, B., Kuppusamy, A., Latchoumi, T. P., Banerjee, A., Sinha, A., Biswas, A., & Subramanian, A. K. (2022). Multi-response Optimization of Turning Parameters for Cryogenically Treated and Tempered WC–Co Inserts. Journal of The Institution of Engineers (India): Series D, 1-12.

Tracking system for birds migration using sensors Bhavya, B., Rajesh, T.R., Latchoumi, T.P., Harika, N., Parthiban, L.A Closer Look at Big Data Analytics, 2021, pp. 195–223

[19] Latchoumi, T. P., & Parthiban, L. (2022). Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wireless Personal Communications, 122(3), 2639-2656.

Banu, J. F., Muneeshwari, P., Raja, K., Suresh, S., Latchoumi, T. P., & Deepan, S. (2022, January). Ontology Based Image Retrieval by Utilizing Model Annotations and Content. In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 300-305). IEEE.

Pavan, V. M., Balamurugan, K., & Latchoumi, T. P. (2021). PLA-Cu reinforced composite filament: Preparation and flexural property printed at different machining conditions. Advanced composite materials.

Shanthi T, Sabeenian RS. Modified Alexnet architecture for classification of diabetic retinopathy images. Computers & Electrical Engineering. 2019 Jun 1;76:56-64.

Garikapati, P. R., Balamurugan, K., Latchoumi, T. P., & Shankar, G. (2022). A Quantitative Study of Small Dataset Machining by Agglomerative Hierarchical Cluster and K-Medoid. In Emergent Converging Technologies and Biomedical Systems (pp. 717-727). Springer, Singapore.

Jeon S, Kim S, Min D, Sohn K. Parn: Pyramidal affine regression networks for dense semantic correspondence. InProceedings of the European Conference on Computer Vision (ECCV) 2018 (pp. 351-366).

Koo E, Kim H. Empirical strategy for stretching probability distribution in neural-network-based regression. Neural Networks. 2021 Aug 1;140:113-20.

Rinartha, K., Suryasa, W., & Kartika, L. G. S. (2018). Comparative Analysis of String Similarity on Dynamic Query Suggestions. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 399-404). IEEE.

Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2021). Get vaccinated when it is your turn and follow the local guidelines. International Journal of Health Sciences, 5(3), x-xv. https://doi.org/10.53730/ijhs.v5n3.2938