| Summary: | 碩士 === 國立勤益科技大學 === 資訊工程系 === 107 === Brand logo detecting is challenging task due to its diversity in size and shape. There are several researchers studied this field and achieved remarkable results using fast region-based convolutional networks and other proposed methods. Although, previous researches are focused on accuracy of the detection only, but not in fast detection field. In contrast, we concerned about real-time logo detection, which can lead to new application opportunities that can be explored by further exploration. In this paper, we extend the state-of-the-art real-time detection architecture YOLOv2 and YOLOv3, and used the FlickrLogos-47 dataset, which is new version of well-known logo dataset, for training to solve the problem. We trained one network for each method. Experiment results show promising results on chosen dataset in real-time detection. In addition, we report a comparison between the methods in mAP metric, show detailed result of each method’s performance on each logo class detection and highlight opportunities and improvements can be explored in future work.
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