Humans can easily detect and identify objects present in an image. The human visual system is fast and accurate and can perform complex tasks like identifying multiple objects and detect obstacles with little conscious thought. For a long time, humans have been trying to make computers understand what is on the images. With the availability of large amounts of data, faster Graphics Processing Unit (GPU)s, and better algorithms, we can now easily train computers to detect and classify multiple objects within an image with high accuracy. The goal of this paper is to implement an object detection model suitable in terms of size and speed to run on an Android device and detect logos in real-time. The proposed approach is based on YOLOv2 (You Only Look Once) state-of-the-art, real-time object detection for logos and this project used the FlickrLogos-32 dataset. Theexperimental results show that we obtained a final accuracy of 82.3% and a speed of 35 fps (frames per second) on the NVidia GeForce GTX 1070
Humans can easily detect and identify objects present in an image. The human visual system is fast and accurate and can perform complex tasks like identifying multiple objects and detect obstacles with little conscious thought. For a long time, humans have been trying to make computers understand what is on the images. With the availability of large amounts of data, faster Graphics Processing Unit (GPU)s, and better algorithms, we can now easily train computers to detect and classify multiple objects within an image with high accuracy. The goal of this paper is to implement an object detection model suitable in terms of size and speed to run on an Android device and detect logos in real-time. The proposed approach is based on YOLOv2 (You Only Look Once) state-of-the-art, real-time object detection for logos and this project used the FlickrLogos-32 dataset. Theexperimental results show that we obtained a final accuracy of 82.3% and a speed of 35 fps (frames per second) on the NVidia GeForce GTX 1070
№ | Author name | position | Name of organisation |
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1 | Primbetov A.. | ! | University of Tashkent for Applied Sciences, |
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Saitov, Renewable energy development in Uzbekistan: current status, problems and solutions,E3S Web of Conferences 216, 01134 (2020), https://doi.org/10.1051/e3sconf/202021601134[31]Sanjar Shoguchkarov, Isroil Yuldoshev, Elyor Saitov and Alisher Boliev, The effect of the surface geometry of a photovoltaic battery on its efficiency, E3S Web of Conferences 216, 01149 (2020), https://doi.org/10.1051/e3sconf/202021601149[32]I.Sapaev, E.Saitov, N.Zoxidov and B.Kamanov, Matlab-model of a solar photovoltaic station integrated with a local electrical network, IOP Conference Series: Materials Science and Engineering, Volume 883, International Scientific Conference Construction Mechanics, Hydraulics and Water Resources Engineering (CONMECHYDRO –2020) 23-25 April 2020, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, Tashkent, Uzbekistan, https://doi:10.1088/1757-899X/883/1/012116[33]Javoxir Toshov and Elyor Saitov, Portable autonomous solar power plant for individual use, E3S Web of Conferences 139, 01087 (2019), https://doi.org/10.1051/e3sconf/201913901087 [34]M. K. Bakhadyrkhanov, S. A. Valiev, N. F. Zikrillaev, S. V. Koveshnikov, E. B. Saitov & S. A. Tachilin, Silicon photovoltaic cells with clusters of nickel atoms, Direct Conversion of Solar Energy into Electric Energy, Published: 22 February 2017. Volume 52, pages 278–281, (2016), https://doi.org/10.3103/S0003701X1604006X |