Deep Learning Logo Detection with Data Expansion by Synthesising Context
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
Logo detection is a challenging task for computer vision, with a wide range of applications in many domains, such as brand logo recognition for commercial research, brand trend research on Internet social community, vehicle logo recognition for intelligent transportation [33, 31, 32, 5, 23, 28]. For generic object detection, deep learning has been a great success [30, 29, 35]. Building a deep object detection model typically requires a large number of labelled training data collected from extensive manual labelling [22, 36]. However, this is not necessarily available in many cases such as logo detection where the publicly available datasets are very small (Table 1). Small training data size is inherently inadequate for learning deep models with millions of parameters. Increasing manual annotation is extremely costly  and unaffordable in most cases, not only in monetary but more critically in timescale terms.
In the current literature, most existing studies on logo detection are limited to small scales, in both the number of logo images and logo classes [18, 23], largely due to the high costs in constructing large scale logo datasets. It is non-trivial to collect automatically large scale logo training data that covers a large number of different logo classes. While web data mining may be a potential solution as shown in other recognition problems [24, 6, 7, 35], it is difficult to acquire accurate logo annotations since no bounding box annotation is available from typical web images and their meta-data.
|Dataset||Logo #||Object #||Image #||PA|
In this work, we present a novel synthetic training data generation algorithm for improving the learning of a deep logo detector with only sparsely labelled training images. This approach enlarges significantly the variations of both logo and its context in the training data without increasing manual labelling effort, so that a deep detector can be optimised to recognise the target logos against diverse and complex background clutters not captured by the original sparse training data. The contributions of this work are: (1) We formulate a novel Synthetic Context Logo (SCL) training data generation method for learning a logo detector given sparsely labelled images. Unlike typical deep learning models, we do not assume the availability of large quantities of labelled training images but only a handful. Our model is designed specifically to augment the training data by enriching and expanding both logos and their context variations. To our knowledge, it is the first attempt of exploiting large scale synthetic training data expansion in context for deep learning a logo detection model. (2) We introduce a large scale automatically synthesised logo dataset, in rich context with labelled logo bounding boxes, consisting of different logos (Figure 1) which is much larger in class number than any existing logo benchmark datasets in the public domain. (3) We further introduce a new logo dataset TopLogo-10, manually collected and labelled from in-the-wild logo images. This TopLogo-10 dataset consists of 10 logo classes of high logo popularity (with high frequency in real-life [11, 1, 8, 17] in rich context, thus more challenging. We evaluated extensively the proposed SCL method for deep learning a logo detector against the state of the art alternatives using two logo benchmark datasets, and provided in-depth analysis and discussion on model generalisation.
2 Related Works
Logo Detection. Most existing approaches to logo detection rely on hand-crafted features, e.g., HOG, SIFT, colour histogram, edge [21, 33, 31, 32, 5, 23, 28]. They are limited in obtaining more expressive representation and model robustness for recognising a large number of different logos. One reason is due to the unavailability of sufficiently large datasets required for exploring deep learning a more discriminative representation. For example, among all publicly available logo datasets, the most common FlickrLogos-32 dataset  contains logo classes each with only images and in total logo objects, whilst BelgaLogos  has logo images from logo classes with bounding box (bbox) location labelled (Table 1).
Nevertheless, a few deep learning based logo detection models have been reported recently. Iandola et al.  applied the Fast R-CNN model  for logo detection, which inevitably suffers from the training data scarcity challenge. To facilitate deep learning logo detection, Hoi et al.  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 , 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.
Synthesising Data Expansion. Generating synthetic training data allows for expanding plausibly ground-truth annotations without the need for exhaustive manual labelling. This strategy has been shown to be effective for training large CNN models particularly when no sufficient training data are available, e.g., Dosovitskiy et al.  used synthetic floating chair images for training optical flow networks; Gupta et al.  and Jaderberg et al.  generated scene-text images for learning text recognition models; Yildirim et al.  exploited deep CNN features optimised on synthetic faces to regress face pose parameters. Eggert et al.  applied synthetic data to train SVM models for company logo detection, which shares the spirit of the proposed method but with an essential difference in that we explore synthetic training data in diverse context variations in the absence of large scale realistic logo dataset in context.
3 Synthetic Logos in Context
Typically, a large training dataset is required for learning an effective deep network . This however is very expensive to collect manually, particularly when manual annotation of locations and bounding boxes of varying-sized objects are needed, e.g., logos in natural images from the wild street views . Given the small size labelled training data in existing logo datasets (Table 1), it is challenging to learn effectively the huge number of parameters in deep models. To solve this problem, we synthesis additional training data. We consider that logo variations are largely due to change of visual context and/or background plus geometric and illumination transforms. We then develop a Synthetic Context Logo (SCL) image generation method for deep learning a logo detection model. By doing so, our model is capable of automatically generating infinite numbers of synthetic labelled logo images in realistic context with little supervision, making deep learning feasible.
In model learning, we exploit a sequential learning strategy by first deploying a large number of synthesised images to pre-train a deep model, followed by fine-tuning the deep model with the sparse manually annotated images. This sequential model training strategy echoes the principle of Curriculum Learning (CL), designed for reducing the difficulties in learning tasks by easy-to-hard staged learning . In learning a logo model, we consider learning from a small number of real images against cluttered background captured in the wild is a more challenging learning task than learning from a large number of synthetic images. We evaluated in our experiments the effectiveness of this staged learning strategy against model training based on a fusion of synthetic and manually annotated images (Section 4.5).
3.1 Logo Exemplar and Context Images
To synthesise images for a given logo class, we need an exemplar image for each logo class. This is obtained from Google Image Search with the corresponding logo name as the query keyword. To minimise any context bias in the synthetic images, we utilised those exemplars with pure logo against a homogeneous transparent background (Figure 1). As such, the surrounding pixels around a logo in any synthetic images are completely determined by the background image, rather than the exemplar images. This is very different from  where (1) pixel-level logo masks are extracted by tedious manual annotation and (2) the appearance of nearby pixels are biased largely towards the source images thus may inevitably break the diversity of surrounding context (see Figure 2 for example).
|Criterion||Clothing ||Luxury ||Fashion ||Streetwear |
Logo Selection. We selected logos by considering: (1) Top popular and luxury brands of clothing/wearable brandname logos based on recent online media reports. Specifically, we particularly explored the clothing brand voting ranking  where the brands “Nike”, “Adidas classic”, “Lacoste” and “Puma” were selected; and the luxury brand ranking  where “Gucci”, “Chanel”, “Prada” and “Michael Kors” were chosen. Also, we picked the top jackets and sports brand “Helly Hansen”  and one of the most popular street clothing brand “Supreme” . Table 2 summarises the popularity ranking of these brand logos. (2) All logo classes in the popular FlickrLogo-32 dataset. (3) Common logos/brands in real-life, such as software, computer, website, food, university. In total, we have logos from a wide range of products, things and places in diverse context. Example logos are shown in Figure 1.
Context Images. For context images, we used the non-logo image subset of the FlickrLogo-32 dataset , obtained from the Flickr website by query keywords “building”, “nature”, “people” and “friends”. These images (Figure 4) present highly diverse natural scene background in which logo may appear during synthesising training data. Moreover, this ensures that all synthesised logo instances are guaranteed to be labelled in the synthesised images, i.e., fully annotated with no missing logos.
3.2 Logo Exemplar Transformation
In addition to context variations, we consider other sources of logo variation are due to illumination change and geometric/perspective transforms. These factors are approximated by warping logo exemplar images. Formally, two independent types of transforms are deployed as follows .
(I) Geometric Transform. Suppose a logo image is on a 3D plane, a general geometric transform of the image is computed as:
where the matrix defines a scaling (Figure 3 (a)) and shearing (Figure 3 (b)) projections; and represent the rotation (Figure 3 (c)) matrices for the corresponding axis respectively, with the angles uniformly sampled from a range of . More precisely, we perform exemplar image geometric transforms in three steps as follows: (i) Random scaling of the exemplar images (Figure 3 (a)); (ii) Random shearing of the scaled images (Figure 3 (b)); (iii) Random rotation of the sheared images (Figure 3 (c)).
(II) Colouring Transform. We also modify the colour appearance of logo exemplar images for synthesising illumination variations. Specifically, we vary the pixel value in the RGB colour space as
where the scalar represents the pixel colour value in any channel and
a random number sampled uniformly from .
The range of is set to 
3.3 Synthesising Context Logo Images
Given the logo exemplar image transforms described above, we generate a number of variations for each logo, and utilise them to synthesis logo images in context by overlaying a transformed logo exemplar at a random location in non-logo context images. This randomness in logo placement provides a large variety of plausible visual scene context to enrich the synthesised logo training images. For every logo class, we generate 100 synthetic logo images in context through randomly selecting context images and applying geometric plus colouring transforms, resulting in 46,300 synthetic context logo training images. Examples of synthetic context logo images are shown in Figure 4.
Two logo detection datasets were utilised for evaluation: FlickrLogo-32  and TopLogo-10, newly introduced in this work.
TopLogo-10. From 463 logos, we selected Top- clothing/wearable brandname logos by popularity/luxury, and constructed a logo dataset with manual labelling. We call this new dataset TopLogo-. Specifically, there are ten logo classes: “Nike”,“Chanel”, “Lacoste”, “Gucci”, “Helly Hansen”, “Adidas Classic”, “Puma”, “Michael Kors”, “Prada”, “Supreme”, with various degrees of composition complexity in the logos. For each logo class, images are included, each with fully manually labelled logo bounding boxes. The exemplars and examples of test images are shown in Figure 6. These logo instances may appear in a variety of context, e.g., shoes, hats, shower gels, wallets, phone covers, lipsticks, eye glasses, spray, jackets, T-shirts, peaked caps, and sign boards. Moreover, logo instances in TopLogo-10 have varying sizes as in natural images, therefore imposes significant detection challenges from small sized logos. TopLogo-10 represents some of the common and natural logo existence scenarios and provides realistic challenges for logo detection.
For each of the two datasets, we divided randomly all the images into two parts: (1) one for training, with 10 images per logo class; (2) one for testing, with the remaining images. Given such a small number of labelled images, it is very challenging to train deep logo models with millions of parameters.
4.2 Baseline Methods
For evaluating the effectiveness of our synthetic context logo images for learning a deep logo detector, we utilised the Faster R-CNN  as our logo detector. Other object detectors [29, 25] are also available but this is independent from our proposed method. We trained a Faster R-CNN detector by the following different processes and comparatively evaluated their logo detection performances. (1) RealImg: Only labelled real training image are used for model training. (2) SynImg-Cls: The synthetic labelled training data from ( for FlickrLogo-32 and for TopLogo-10) target logo classes are used for model training; (3) SynImg-Cls: The synthetic labelled training data from all logo classes are used for model training; (4) SynImg-Cls+RealImg: We first utilise the synthetic training data from the target logo classes to pre-train a Faster R-CNN, then fine-tune the model using labelled real training data; this is a staged curriculum learning model training scheme ; (5) SynImg-Cls+RealImg: Similar to SynImg-Cls+RealImg but with a difference that all synthetic training data are used for pre-training the detector; (6) SynImg-Cls+RealImg (Fusion): Similar to SynImg-Cls+RealImg but with a difference that the model is trained in a single step using the fusion of synthetic and realistic training data, other than the sequential curriculum learning.
4.3 Evaluation Metrics
All models are trained on a training set and evaluated on a separate independent testing set. The logo detection accuracy is measured by Average Precision (AP) for each individual logo class and mean Average Precision (mAP) over all logo classes .
4.4 Implementation Details
For learning a logo Faster R-CNN detector, we set the learning rate as on either synthetic or real training data. The learning iteration is set to . For all the models, we (1) pre-trained a Faster R-CNN with the training data of the ImageNet-1K object classification dataset  for parameter initialisation , and (2) then further fine-tuned the model on PASCAL VOC non-logo object detection images .
|(per logo class)||Ritt||Shell||Sing||Starb||Stel||Texa||Tsin||Ups|
|RealImg||Training: 10 RealImg||79.0||25.8||61.2||44.2||45.9||80.6||64.3||43.2|
|Test: 60 RealImg||34.8||45.8||71.8||70.2||79.6||56.7||56.9||52.2||50.4|
|SynImg-Cls||Training: 100 SynImg||6.1||11.1||4.1||44.7||22.9||60.9||43.6||28.8|
|Test: 60 RealImg||22.7||38.3||15.5||65.6||28.7||55.1||27.4||20.1||27.6|
|SynImg-Cls||Training: 100 SynImg||0.4||3.8||4.8||48.7||20.5||45.2||29.0||24.5|
|Test: 60 RealImg||18.6||38.6||0.7||49.3||28.5||61.1||23.7||17.5||20.5|
|SynImg-Cls||Training: 100 SynImg||76.0||31.5||63.0||52.2||54.3||90.0||84.0||46.6|
|+ RealImg||+10 RealImg||58.0||52.6||65.2||23.2||24.0||12.5||54.1||23.6|
|Test: 60 RealImg||37.9||45.6||75.0||73.8||79.0||64.2||57.4||54.4||54.8|
|SynImg-Cls||Training: 100 SynImg||82.7||33.7||67.2||53.5||50.8||85.6||72.4||51.3|
|+ RealImg||+ 10 RealImg||59.6||67.7||69.6||28.1||21.9||17.4||59.6||21.8|
|Test: 60 RealImg||42.7||45.5||74.0||72.3||83.1||63.6||60.2||49.3||55.9|
|SynImg-Cls||Training: 100 SynImg||10.6||14.0||5.5||56.9||28.4||62.9||48.2||53.6|
|+ RealImg||+ 10 RealImg||44.1||21.1||47.1||10.6||6.2||5.2||52.9||15.0|
|(Fusion)||Test: 60 RealImg||37.7||36.8||4.5||59.4||25.1||67.4||27.7||22.4||30.9|
|RealImg||Training: 40 RealImg||90.9||77.4||90.9||88.6||71.1||91.0||98.3||86.2|
|Test: 30 RealImg||81.0||57.3||97.9||99.5||86.7||90.4||87.5||85.8||81.1|
|Deep Logo ||Training: 40 RealImg||92.9||53.5||80.1||88.8||61.3||90.0||84.2||79.7|
|Test: 30 RealImg||63.0||57.4||94.2||95.9||82.2||87.4||84.3||81.5||74.4|
|BD-FRCN-M ||Training: 40 RealImg||-||-||-||-||-||-||-||-|
|Test: 30 RealImg||-||-||-||-||-||-||-||-||73.5|
Evaluation on FlickrLogo-32. We performed logo detection on the FlickrLogo-32 logo images . The results of different methods are shown in Table 3. It is evident that logo detection performance by Faster R-CNN can be largely improved by expanding the training data with the synthetic context logo images generated with our proposed method. For example, the combination of full synthetic and realistic training data (i.e., SynImg-Cls + RealImg) can boost the detection accuracy from (by RealImg) to , with increase in absolute mAP or relative improvement. Importantly, such performance gain is achieved without any additional exhaustive labelling but by automatically enriching the context variations in the training data.
Specifically, we draw these following observations. Firstly, by using merely 10 training images per logo (i.e., RealImg), Faster R-CNN is already able to achieve fairly good detection results ( in mAP). This suggests the great capability of deep models partially due to the good parameter initialisation on ImageNet and PASCAL VOC images, confirming the similar findings elsewhere [15, 3, 2]. Secondly, when using our synthetic training images alone (i.e., SynImg-Cls and SynImg-Cls), the model performance on test data is much inferior than using sparse realistic training data. The potential reasons is that, there may exist great discrepancy between realistic and synthetic training images, also known as the domain drift problem [37, 26], i.e., a trained model may degrade significantly in performance when deployed to a new domain with much disparity involved as compared to the training data. Thirdly, it is observed interestingly that synthetic training data from non-target logo classes may even hurt the model generalisation, when comparing the mAP result between SynImg-Cls () and SynImg-Cls (). This may be due to the distracting effects introduced during detector optimisation on a large number of () non-target logos and thus making the resulting model less effective towards target logos in model deployment. Fourthly, it is also found that model pre-training on the full synthetic images turns out to be superior than on those of target logo classes alone. This suggests that more generic model pre-training may produce better initialisation for further incremental model adaptation on sparse real training data.
|SynImg-Cls + RealImg||51.9||44.8||41.1||38.1||53.3||52.5||11.8||28.9||18.4||63.6||40.4|
|SynImg-Cls + RealImg||52.7||39.9||49.7||36.5||48.4||62.7||14.8||29.8||18.6||64.6||41.8|
|SynImg-Cls + RealImg (Fusion)||25.4||11.8||4.7||0.6||17.9||44.2||1.6||0.9||15.5||40.3||16.3|
In addition to comparative evaluations under the same setting, we also compare the reported results from the state-of-the-art methods [18, 27]. It can be seen (last three rows in Table 3) that the best alternative DeepLogo surpasses our method (SynImg-463Cls + RealImg): vs. in mAP. However, it should also be pointed out that our model was trained on much less () real training images. To provide a more comparable evaluation, we exploited the Faster R-CNN model as an alternative to DeepLogo, since DeepLogo code is not released publicly, and trained a new model using same training data split as in DeepLogo. Our results on the FlickrLogo-32 dataset under the DeepLogo image split by our Faster RCNN based logo detector is (the third last row in Table 3), significantly better than DeepLogoâs . Moreover, under our split, a Faster R-CNN based logo detector (RealImg in Table 3) yields in mAP vs. by our method SynImg-463Cls + RealImg. Taken both into account, it suggests that the proposed method is more likely to outperform the DeepLogo model if compared under the same split setting. Given that this work focuses on exploring synthetic training data for deep learning logo detection regardless the detection model, any other detector  can be readily integrated into our framework.
We further evaluated the effect of the curriculum learning strategy in detection performance. To that end, we can compare SynImg-Cls + RealImg and SynImg-Cls + RealImg (Fusion) in Table 3. It is evident that by blindly learning a Faster R-CNN logo detector on the fusion (combined) of real and synthetic training images, the model generalisation is significantly degraded, likely due to severe bias towards the synthetic data. This is because that the synthetic images dominate in numbers in combined training data, resulting in that the trained model suffers from the domain shift problem. This result validates the efficacy of the proposed curriculum learning method in learning a more robust logo detection model when given heterogeneous training data sources.
Lastly, we carried out qualitative evaluations by examining the effect of synthetic context logo images on the detector performance. Figure 7 shows that context variations/diversity is effective for improving deep model learning resulting in more discriminative logo features therefore less missing logo detections – less false negatives (see and columns). It is also evident that both models have difficulties in detecting small logos ( column) and logos under extreme lighting conditions ( column). Also, the model trained by a single stage with full synthetic and real training data is likely to generate more false positive detections ( column), likely due to over-diversity (aka noise) introduced by the synthetic context.
|Scene Context + RealImg||51.9||44.8||41.1||38.1||53.3||52.5||11.8||28.9||18.4||63.6||40.4|
|Clean Context + RealImg||48.7||37.3||38.1||33.9||46.4||61.7||2.5||31.0||12.4||67.4||37.9|
|No Context + RealImg||28.6||32.9||32.8||33.9||47.1||40.4||0.5||15.0||9.5||44.4||28.5|
Evaluation on TopLogo-10. We evaluated Faster R-CNN detectors trained with different methods on our TopLogo-10 dataset. We present the detection results in Table 4.
By exploiting our synthetic context logo training images, the logo detection performance can be improved more significantly than on FlickrLogo-32, e.g., from (by RealImg) to (by SynImg-Cls + RealImg) with / absolute/relative boost in mAP, as compared to on FlickrLogo-32. This further suggests the effectiveness of our synthetic context driven training data expansion method for logo detection, particularly for the practical and more challenging clothing brand logos. Mostly, we observed similar phenomenons as those on FlickrLogo-32 but one difference that SynImg-Cls outperforms considerably SynImg-Cls. The possible reason is that, the generic semantic context learned from a large number of logo classes becomes more indicative and useful since the background of clothing logos tends to be more clutter than those from FlickrLogo-32. Similarly, we show some qualitative detection examples in Figure 8.
Further Analysis. To give more insight, we performed further experiments with SynImg-Cls on the TopLogo-10 dataset.
The Effect of Different Synthetic Context. We specifically evaluated the impact of context on model learning. To that end, we introduce a new type of context - clean black context (“Clean Context”) as an alternative to the natural scene context (“Scene Context”) used early in our SCL model. Table 5 shows when only synthetic training images were used in model training, the “Scene Context” is much more superior than the “Clean Context” for model training. This advantage remains when real training images were exploited for model adaptation. Interestingly, we also found that the “Clean Context” is able to improve logo detection performance, as revealed by comparison to the results without using any synthetic context. This further suggests that synthetic context based data expansion is an effective strategy for addressing the training data scarcity challenge.
The Effect of Individual Logo Transform. We evaluated the impact of different logo image transforms. To that end, we eliminated selectively each specific transform in synthesising the training images and then evaluated any change in model performance. Table 6 shows that: (1) With synthetic training images alone, all geometric and colour transforms except rotation bring some benefits, especially shearing and scaling. The little difference made by rotation makes sense considering that logo objects appear mostly without any rotation in real scenes. (2) The benefit of each transform remains after the detector is fine-tuned on real training data.
In this work, we described a new Synthetic Context Logo (SCL) training image generation algorithm capable of improving model generalisation capability in deep learning a logo detector when only sparse manually labelled data is available. We demonstrated the effectiveness and superiority of the proposed SCL on performing logo detection on unconstrained images, e.g., boosting relatively the detection accuracy of state-of-the-art Faster R-CNN network model by on FlickrLogo-32 and on the more challenging TopLogo-10 benchmark datasets. Importantly, this performance boost is obtained without the need for additional manual annotation. It shows the effectiveness of expanding training data through synthesising pseudo data especially with rich logo context variations. As such, deep detection model can be learned more reliably with better robustness against complex background clutters during model deployment. We carried out detailed evaluation and analysis on different strategies for model training. We further introduced a new logo dataset TopLogo-10, consisting of top 10 most popular clothing/wearable logos in challenging visual scene context, designed for more realistic testing of logo detections in real-world applications.
- The TopLogo-10 dataset is available at: http://www.eecs.qmul.ac.uk/~hs308/qmul_toplogo10.html
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