Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs. In this work, we describe a model training image synthesising method capable of improving significantly logo detection performance when only a handful of (e.g., 10) labelled training images captured in realistic context are available, avoiding extensive manual labelling costs. Specifically, we design a novel algorithm for generating Synthetic Context Logo (SCL) training images to increase model robustness against unknown background clutters, resulting in superior logo detection performance. For benchmarking model performance, we introduce a new logo detection dataset TopLogo-10 collected from top 10 most popular clothing wearable brandname logos captured in rich visual context. Extensive comparisons show the advantages of our proposed SCL model over the state-of-the-art alternatives for logo detection using two real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-10.
Nevertheless, a few deep learning based logo detection models have been reported recently. @cite_10 applied the Fast R-CNN model @cite_29 for logo detection, which inevitably suffers from the training data scarcity challenge. To facilitate deep learning logo detection, @cite_12 built a large scale dataset called LOGO-Net by exhaustively collecting images from online retailer websites and then manually labelling them. This requires a huge amount of construction effort and moreover, LOGO-Net is inaccessible publicly. In contrast to all existing attempts above, we explore the potentials for learning a deep logo detection model by synthesising a large scale sized training data to address the sparse data annotation problem without additional human labelling cost. Compared to @cite_12 , our method is much more cost-effective and scalable for logo variations in diverse visual context, e.g., accurate logo annotation against diverse visual scene context can be rapidly generated without any manual labelling, and potentially also for generalising to a large number of new logo classes with minimal labelling.
@cite_29 @cite_10 @cite_12
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Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset. Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at this http URL