Nowadays, people have entered the era of big data for security. The scale of data generated by security systems every day is very large. The importance of artificial intelligence for security lies in its ability to quickly obtain valuable information from massive data through the use of deep learning, feature extraction, object recognition and other technologies, thus transforming the monitoring system from traditional post investigation processing into in-process control, and even proactive pre-warning, At the same time, the staff are also free from heavy monitoring tasks. The security system is widely used in all walks of life. Therefore, this paper combines the general deep learning to build the technical research on vehicle recognition in intelligent security. The purpose of this paper is to study the technology of vehicle recognition in intelligent security. Analysis of experimental results from the above results, we can see that ResNet-50 can solve the task well, and it has achieved higher accuracy and better robustness.

Moju Du. Deep Learning Technology for Vehicle Recognition in Intelligent Security. International Journal of Neural Network (2020), Vol. 1, Issue 3: 9-17. https://doi.org/10.38007/NN.2020.010302.

[1] Nadbystrzycka, Lublin, Poland. Errors in controlling cars cause tragic accidents involving motorcyclists. Open Engineering, 2020, 11(1):1025-1033. https://doi.org/10.1515/eng-2020-0099

[2] Gupta A, Ali R, Singh A P, et al. Autonomous Recognition Model. International Journal of Innovative Technology and Exploring Engineering, 2020, 10(6):67-73. https://doi.org/10.35940/ijitee.F8821.0410621

[3] Eoa B, Jbb C, Zz D, et al. Car style-holon recognition in computer-aided design. Journal of Computational Design and Engineering, 2019, 6(4):719-738. https://doi.org/10.1016/j.jcde.2018.10.005

[4] Hao, yan, Xuetao, et al. The design research of an intelligent vehicle-mounted/maintenance alarm system based on image recognition technology. IOP Conference Series: Earth and Environmental Science, 2019, 233(3):32009-32009. https://doi.org/10.1088/1755-1315/233/3/032009

[5] Fitriati D, Pasha N R, Hariyanto B, et al. Smart System For Automatic Crop And Recognition Plat Number. Jurnal Riset Informatika, 2020, 3(2):145-152. https://doi.org/10.34288/jri.v3i2.183

[6] Bluntzer J B, Ostrosi E. From the Car Style Pregnancy towards the Brand Country Origin Recognition. Proceedings of the Design Society International Conference on Engineering Design, 2019, 1(1):3901-3910. https://doi.org/10.1017/dsi.2019.397

[7] Elbagoury B M, Maskeliunas R, Salem A. A hybrid liar/radar-based deep learning and vehicle recognition engine for autonomous vehicle Precrash control. Eastern-European Journal of Enterprise Technologies, 2018, 5(9 (95)):6-17. https://doi.org/10.15587/1729-4061.2018.141298

[8] Stoll T, Imbsweiler J, Deml B, et al. Three Years CoInCar: What Cooperatively Interacting Cars Might Learn from Human Drivers. IFAC-PapersOnLine, 2019, 52(8):105-110. https://doi.org/10.1016/j.ifacol.2019.08.056

[9] Svatiuk D, Svatiuk O, Belei O. Application Of The Convolutional Neural Networks For The Security Of The Object Recognition In A Video Stream. Cybersecurity Education Science Technique, 2020, 4(8):97-112. https://doi.org/10.28925/2663-4023.2020.8.97112

[10] Taee E. The proposed iraqi vehicle license plate recognition system by using prewitt edge detection algorithm. Journal of Theoretical and Applied Information Technology, 2018, 96(10):2754-2764.

[11] Fedorchenko I, Oliinyk A, Stepanenko A, et al. Development of the modified methods to train a neural network to solve the task on recognition of road users. Eastern-European Journal of Enterprise Technologies, 2019, 2(9 (98)):46-55. https://doi.org/10.15587/1729-4061.2019.164789

[12] CGP Suescún, Murillo P, Moreno R J. Scratch Detection in Cars Using a Convolutional Neural Network by Means of Transfer Learning. International Journal of Applied Engineering Research, 2018, 13(16):12976-12982.

[13] Omran S S, Jarallah J A. Automatic Iraqi Cars Number Plates Extraction. Iraqi Journal for Computers and Informatics, 2018, 44(1):34-41. https://doi.org/10.25195/ijci.v44i1.111

[14] Elhousni M, Lyu Y, Zhang Z, et al. Automatic Building and Labeling of HD Maps with Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(8):13255-13260. https://doi.org/10.1609/aaai.v34i08.7033

[15] Balakrishnan A, Rajan A, Salter A I, et al. Multispecific Targeting with Synthetic Ankyrin Repeat Motif Chimeric Antigen Receptors. Clinical Cancer Research, 2019, 25(24):7506-7516. https://doi.org/10.1158/1078-0432.CCR-19-1479

[16] Khan S, Rahmani H, Shah S, et al. A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision, 2018, 8(1):1-207. https://doi.org/10.1007/978-3-031-01821-3

[17] Viebke A, Memeti S, Pllana S, et al. CHAOS: a parallelization scheme for training convolutional neural networks on Intel Xeon Phi. Journal of Supercomputing, 2019, 75(1):197-227. https://doi.org/10.1007/s11227-017-1994-x

[18] As I, Pal S, Basu P. Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 2018, 16(4):306-327. https://doi.org/10.1177/1478077118800982