Keywords: object detection, Convolutional Neural Network (cnn), You Only Look Once (yolo), Faster r-cnn (Region-based Convolutional Neural Networks, ssd

Object detection using deep learning and neural networks has taken some massive leaps in the past couple of years, and the field is very popular at the moment. Every month someone releases a new research paper, a new algorithm or a new solution for a certain problem. It is just the basic and primary idea that I have discussed and carried my research on. Logo recognition is a key problem in marketing analytics, digital advertising, and augmented reality. The purpose of this paper was to create a real-time logo detection system for android mobile using YOLOv2. I have trained the model on the FlickrLogos-32 dataset and experiment results to show that YOLOv2 performs very well in real-time logo detection. By performing a comprehensive analysis of YOLOv2 over FlickrLogos-32 dataset, we found that the experiment result showed that we managed to achieve a final mean average precision (mAP) 82.53 % and 30-35 FPS (frames per second) speed on an NVIDIA GeForce Gtx 1070 and our models performed well at the detection, with very low false-positive rates possible for a fairly reasonably. The application runs smoothly on the current test hardware. However, the main part of the goal was successfully implemented, a working application that utilizes a neural network model for object detection.

References

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5. Bianco, S., Buzzelli, M., Mazzini, D., & Schettini, R. (2017). Deep learning for logo recognition. Neurocomputing, 245, 23-30.

6.