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Abbaz Primbetov, Automatic Red Eye Remover using OpenCV , Modern Science and Research: Vol. 3 No. 1 (2024): Modern science and research
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REAL TIME LOGO RECOGNITION USING YOLO ON ANDROID
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. The experimental 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.
