2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 4 , Issue 2 , PP: 69-98, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Ossama Embarak 1 , Mhmed Algrnaodi 2
Doi: https://doi.org/10.54216/JISIoT.040202
Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.
Image Detection System Design, Internet of Things (IoT), Cloud Computing, Improved Inter-Frame Difference Method
[1] Melvin, J. . (2019). Industry player eyes faster, expanded sharing of cyber threat information. Platts Megawatt Daily, 24(32), 2-3.
[2] Orth, A. , Wilson, E. R. , Thompson, J. G. , & Gibson, B. C. . (2018). A dual-mode mobile phone microscope using the onboard camera flash and ambient light. Scientific Reports, 8(1), 3298.
[3] Lee, H. , & Kim, D. K. . (2019). Real-time mobile emotional content player using smartphone camera-based ppg measurement. Mobile Information Systems, 2019(1), 1-12.
[4] Jin, Y. , Gao, H. , Hu, T. , & Li, X. . (2019). Special issue on AI-driven smart networking and communication for personal internet of things, part Ⅰ. International journal of wireless information networks, 26(3), 131-132.
[5] Zhou, Z. , Akhtar, Z. , Man, K. L. , & Siddique, K. . (2019). A deep learning platooning-based video information-sharing internet of things framework for autonomous driving systems. International Journal of Distributed Sensor Networks, 15(11), 2019.
[6] Jang, J. , Shin, M. , Lim, S. , Park, J. , & Paik, J. . (2019). Intelligent image-based railway inspection system using deep learning-based object detection and Weber contrast-based image comparison. Sensors, 19(21), 4738.
[7] Hsu, F. H. , & Shen, C. A. . (2019). The design and implementation of an embedded real-time automated ic marking inspection system. IEEE Transactions on Semiconductor Manufacturing, 32(1), 112-120.
[8] Lin, X. , Wang, J. , Zhou, Y. , & Lin, C. . (2018). A rapid 3d vision inspection system for sheet metal parts based on feature extraction and partial point clouds. Iop Conference, 452(2), 022137.
[9] Jawahar, M. , Vani, K. , & Babu, N. K. C. . (2019). Machine vision inspection system for detection of leather surface defects. The Journal of the American Leather Chemists Association, 114(1), 10-19.
[10] Kumar, S. , & Sisodia, V. S. . (2019). Real time digital image processing system for quality control in paper manufacturing industry. IOSR Journal of Electronics and Communication Engineering, 14(1), 55-60.
[11] Wen, Bor-Jiunn, & Huang, Shih-An. (2019). Residual stress image inspections based on bending testing for flexible transparent conducting substrates by single-direction common-path image interferometry. IEEE sensors journal, 19(5), 1701-1709.
[12] Fonseka, C. L. S. C. , & Jayasinghe, J. A. K. S. . (2019). Implementation of an automatic optical inspection system for solder quality classification of tht solder joints. Components, Packaging and Manufacturing Technology, IEEE Transactions on, 9(2), 353-366.
[13] Zhang, H. , Li, X. , Zhong, H. , Yang, Y. , & Wang, Y. . (2018). Automated machine vision system for liquid particle inspection of pharmaceutical injection. IEEE Transactions on Instrumentation and Measurement, PP(99), 1-20.
[14] Teng, Y. , Chen, S. , Deng, J. , Ma, Y. , & Zhong, L. . (2019). Application of intelligent inspection robot system in sutong gil utility tunnel project. Gaodianya Jishu/high Voltage Engineering, 45(2), 393-401.
[15] Sarkar, S. , Chatterjee, S. , & Misra, S. . (2018). Assessment of the suitability of fog computing in the context of internet of things. IEEE Transactions on Cloud Computing, 6(1), 46-59.
[16] Rajiv, R. , Omer, R. , Surya, N. , Mazin, Y. , Philip, J. , & Zhenya, W. , et al. (2018). The next grand challenges: integrating the internet of things and data science. IEEE Cloud Computing, 5(3), 12-26.
[17] Ferrández-Pastor Francisco, García-Chamizo Juan, Mario, N. H. , & Mora-Martínez José. (2018). Precision agriculture design method using a distributed computing architecture on internet of things context. Sensors, 18(6), 1731.
[18] Li, H. , Ota, K. , & Dong, M. . (2018). Learning IOT in edge: deep learning for the internet of things with edge computing. IEEE Network, 32(1), 96-101.
[19] Qiu, T. , Chen, N. , Li, K. , Atiquzzaman, M. , & Zhao, W. . (2018). How can heterogeneous internet of things build our future: a survey. Communications Surveys & Tutorials, 20(3), 2011-2027.
[20] Wolf, M. , & Serpanos, D. . (2018). Safety and security in cyber-physical systems and internet-of-things systems. Proceedings of the IEEE, 106(1), 9-20.
[21] Nayar, K. B. , & Kumar, V. . (2018). Cost benefit analysis of cloud computing in education. International journal of business information systems, 27(2), 205-221.
[22] Wang, Y. , Li, J. , & Wang, H. H. . (2019). Cluster and cloud computing framework for scientific metrology in flow control. Cluster Computing, 22(1), 1-10.
[23] Liu, X. F. , Member, S. , IEEE, Zhan, Z. H. , & Member. (2018). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 22(1), 113-128.
[24] Wang, H. , Xiao, G. , Wei, Z. , & Hai, T. . (2019). Network optimisation for improving security and safety level of dangerous goods transportation based on cloud computing. International journal of information and computer security, 11(2), 160-177.
[25] Bauer, E. . (2018). Improving operational efficiency of applications via cloud computing. IEEE Cloud Computing, 5(1), 12-19.
