Super Resolution Generative Adversarial Networks for Image Supervise Learning
DOI:
10.33395/sinkron.v7i2.11373Keywords:
Generative Adversarial Networks, Convolutional Neural Network, Image Plate Number Vehicle, Deep Learning, Computer VisionAbstract
The E-Tilang application system has been widely used to support modern traffic, whereas protocol roads in big cities in Indonesia are already widely used. In principle, the plate number detection tool uses image recognition for detection. Image number plates on vehicles cannot always be read clearly, this is what causes the detection method to be a problem if the image plate number is further processed. The method for processing the plate number image uses deep learning and computer vision methods. For the condition of the image plate number that is not clear, the process of improving the image resolution from low resolution to high resolution is carried out, by applying Generative Adversarial Networks. This method consists of two main parts, namely Generate and Discriminator. Generate serves to generate an image and the Discriminator here is to check the image, can the image plate number be read or not? So that if the image plate number cannot be read, then the process is carried out again to the Generator until it is received by the Discriminator to be read. The process does not end here, the results will be carried out in the next process using Convolutional Neural Networks. Where the process is to detect the plate number image according to the classification of the plate number according to the region. The point is that an unclear image becomes clear by increasing the resolution from low resolution to high resolution so that it is easily read by the Convolutional Neural Network (CNN) algorithm so that the image is easily recognized by the CNN Algorithm. This becomes important in the CNN algorithm process because it gets the processed dataset. To produce a good model, preprocessing of the dataset is carried out. So that the model can detect the image well in terms of model performance.
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Abu-Srhan, A., Abushariah, M. A. M., & Al-Kadi, O. S. (2022). The effect of loss function on conditional generative adversarial networks. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2022.02.018
Bode, M., Gauding, M., Lian, Z., Denker, D., Davidovic, M., Kleinheinz, K., Jitsev, J., & Pitsch, H. (2021). Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows. Proceedings of the Combustion Institute, 38(2), 2617–2625. https://doi.org/10.1016/j.proci.2020.06.022
Hendriyana, H., & Yazid Hilman Maulana. (2020). Identification of Types of Wood using Convolutional Neural Network with Mobilenet Architecture. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 70–76. https://doi.org/10.29207/resti.v4i1.1445
Jiang, M., Zhi, M., Wei, L., Yang, X., Zhang, J., Li, Y., Wang, P., Huang, J., & Yang, G. (2021). FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution. Computerized Medical Imaging and Graphics, 92(April), 101969. https://doi.org/10.1016/j.compmedimag.2021.101969
Kulkarni, U., Meena, S. M., Gurlahosur, S. V., & Bhogar, G. (2021). Quantization Friendly MobileNet (QF-MobileNet) Architecture for Vision Based Applications on Embedded Platforms. Neural Networks, 136, 28–39. https://doi.org/10.1016/j.neunet.2020.12.022
Li, S., Jiang, H., & Pang, W. (2017). Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading. Computers in Biology and Medicine, 84, 156–167. https://doi.org/10.1016/j.compbiomed.2017.03.017
Lin, C., Zhang, S., You, S., Liu, X., & Zhu, Z. (2021). Real-time foreground object segmentation networks using long and short skip connections. Information Sciences, 571, 543–559. https://doi.org/10.1016/j.ins.2021.01.044
Liu, Y. S., Huang, C. H., & You, S. D. (2021). Estimation of channel MSE for ATSC 3.0 receiver and its applications. ICT Express, xxxx, 1–5. https://doi.org/10.1016/j.icte.2021.09.001
Liu, Y., Wang, X., Wang, L., & Liu, D. (2019). A modified leaky ReLU scheme (MLRS) for topology optimization with multiple materials. Applied Mathematics and Computation, 352, 188–204. https://doi.org/10.1016/j.amc.2019.01.038
Matsunobu, L. M., Pedro, H. T. C., & Coimbra, C. F. M. (2021). Cloud detection using convolutional neural networks on remote sensing images. Solar Energy, 230(May), 1020–1032. https://doi.org/10.1016/j.solener.2021.10.065
Moran, M. B. H., Faria, M. D. B., Giraldi, G. A., Bastos, L. F., & Conci, A. (2021). Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs. Computers in Biology and Medicine, 129(July 2020), 104139. https://doi.org/10.1016/j.compbiomed.2020.104139
Mukhopadhyay, A. K., Majumder, S., & Chakrabarti, I. (2022). Systematic realization of a fully connected deep and convolutional neural network architecture on a field programmable gate array. Computers and Electrical Engineering, 97(October 2020), 107628. https://doi.org/10.1016/j.compeleceng.2021.107628
Muralidharan, N., Gupta, S., Prusty, M. R., & Tripathy, R. K. (2022). Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network. Applied Soft Computing, 119, 108610. https://doi.org/10.1016/j.asoc.2022.108610
Qi, H., Xiao, S., Shi, R., Ward, M. O., Chen, Y., Tu, W., Su, Q., Wang, W., Wang, X., & Zhang, Z. (2018). DEEP MULTI-SCALE VIDEO PREDICTION BEYOND MEAN SQUARE ERROR. In Nature (Vol. 388, pp. 539–547).
Suartika E. P, I. W., Wijaya, A. Y., & Soelaiman, R. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. Jurnal Teknik ITS, 5(1), 76. http://repository.its.ac.id/48842/
Sun, J., Li, X., Tang, C., Wang, S. H., & Zhang, Y. D. (2021). MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images[Formula presented]. Knowledge-Based Systems, 232, 107494. https://doi.org/10.1016/j.knosys.2021.107494
Udayana, I. P. A. E. D., Sudarma, M., & Ariyani, N. W. S. (2021). Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(3), 186. https://doi.org/10.24843/lkjiti.2021.v12.i03.p06
Upadhyay, P. K., Rastogi, S., & Kumar, K. V. (2022). Coherent convolution neural network based retinal disease detection using optical coherence tomographic images. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2021.12.002
Yang, L., Chen, W., Liu, W., Zha, B., & Zhu, L. (2020). Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets. IEEE Access, 8(12), 30271–30286. https://doi.org/10.1109/ACCESS.2020.2972464
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