## 5 Conclusions  
We have proposed a Dual-Attention-based LogoNet Network, using spatial and channel attention modules. Our architecture refines output feature maps and improves the performance with an accuracy gain of 1.8% in a considerable computation time. Furthermore, we propose a lightweight CNNs method with anchor-free detector. We also propose an adversarial learning-based domain adaptation approach to align the detection network between source and target domains. In future, we will discover more attention- and domain adaptation-based mechanisms including transformer [40] and lightweight compact network for logo detection in real-time.  
In this paper, we propose Dual-Attention LogoNet. The backbone architecture of the proposed method includes a densely layer-aggregated hourglass-like network. Spatial and channel attention modules are added to further refine the feature maps. The CenterNet detention head is used [14].  
Our key contributions are as follows:  
(1) We propose an attention-based architecture called LogoNet, which includes a backbone feature extraction framework that aggregates feature maps at different scales. This framework efficiently extracts feature information from different scales and also prevents loss of information during spatial resolution scaling.  
(2) The proposed spatial attention module enhances attention to identify target objects. This attention module refines the output feature maps. It serves as a tool to focus on the logo regions.  
A preliminary version of this work was presented as a five-page conference paper at IEEE International Conference on Consumer Electronics-2021 [15]. As an extension, here, we introduce a dual attention-based method by employing a channel attention module along with the spatial attention module, a lightweight CNN architecture, and a domain optimization-based approach. Our new contributions are as follows.  
(3) The channel attention module is combined with the new proposed architecture in a different and effective manner to make it more efficient.  
(4) We propose a lightweight CNNs architecture with a reduced number of network parameters and computation complexity. The architecture can boost the run-time associated with the inference of network while maintaining the performance.  
(5) We propose an adversarial learning-based domain adaptation approach to generalize the network from source to target domain. We propose to use the mid-level output feature maps of the feature extraction network instead of using class-wise heatmaps, which is commonly used in most of the previously proposed domain adaptation based methods. To the best of our knowledge, this the first domain discriminator network-based adversarial learning scheme employed with an anchor-free detector.  
Conceptualization, R.K.J., Y.-W.C., X.R.; methodology, software, validation, formal analysis, investigation, R.K.J., T.W., T.N., T.S.; resources, Y.-W.C.; data curation, R.K.J.; writing--original draft preparation, R.K.J.; writing--review and editing, visualization, Y.-W.C., X.R., Y.I.; supervision, Y.-W.C., X.R.; project administration, Y.-W.C., X.R.; funding acquisition, Y.-W.C., X.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.