## 3 Experiments
### Evaluation on FlickrLogos-32 Dataset  
Logo images of FlickrLogos-32 [34] dataset were used for training. FlickrLogos-32 dataset has 32 logo classes. Each class contains 70 images for experiments. For each class, we consider 30 images for training, 10 images for validation and 30 images for test. There were a total of 1602 logo objects in 960 test images for different categories.  
Table 3 shows the details of ablation study on FlickrLogo-32 dataset. According to the results, the mAP accuracy is slightly improved when we aggregate feature maps at different scales (Proposed Method 1 ) or when we employ spatial attention module with baseline network (Proposed Method 2). When we implement spatial attention module with layer-aggregated feature maps together, detection accuracy improves effectively (LogoNet--Proposed Method 3). The calculation of the channel-wise response further improves the detection accuracy (Dual-Attention LogoNet-- Proposed Method 4). We observe effectiveness of our methods in two steps: (i) the aggregation of feature maps at different scale, improves the global feature representation, (ii) combining attention modules with network generates a balanced and robust feature map with significant visual and semantic detail.  
Table 4 reports mAP and detection time using different detectors on Flickr32 dataset. These methods are: Faster R-CNN with ResNet50, SSD with VGG16, CenterNet with HourglassNet, CenterNet with SENet HourglassNet [8], CenterNet with ECANet HourglassNet [20], CenterNet: Channel attention module [22] added with our proposed spatial attention module and backbone network, LogoNet, Dual-Attention LogoNet.  
SSD achieves 76.6% accuracy in mAP with the faster detection time of 0.0531 s. Faster R-CNN has 81.0% accuracy with a 0.1115 s inference time. CenterNet with HourGlass achieves 80.7% accuracy and uses 0.1083 s detection time. There is a slight drop in the performance of CenterNet-HourGlass with SENet and ECANet block. These approaches have 80.2% and 79.0% accuracy with 0.1354 s and 0.1260 s detection time, respectively. Channel attention module [22] employed with our proposed spatial attention module and backbone network improves the accuracy by around 0.7% In comparison to baseline method. Whereas, detection time taken is relatively higher (0.2010 s per image) for this approach. LogoNet shows a significant improvement in performance over the conventional methods with a considerable detection time. LogoNet has 82.2% mAP accuracy with 0.1145 s inference time. Meanwhile, our proposed Dual-Attention LogoNet yields an improved performance with the 82.5% mAP and 0.1166 s detection time. The logo detection performance is depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Layer-** & **Spatial** & **Channel** & \\  & **Aggregation** & **Attention** & **Attention** & **mAP** \\ \hline CenterNet (baseline) & & & & 80.7 \\ Proposed Method 1 & ✓ & & & 81.0 \\ Proposed Method 2 & & ✓ & & 80.8 \\ Proposed Method 3 & ✓ & ✓ & & 82.2 \\ Proposed Method 4 & ✓ & ✓ & ✓ & 82.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation Experiments on FlickrLogos-32 Dataset.