# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 5 Conclusions  
To address the problem of container number detection in natural scenes, we propose DAMNet. This method utilizes the efficiency of the ResNet18 backbone network for feature extraction and introduces the EMA, enabling the model to focus on key features. To further enhance feature extraction, we design an auxiliary information branch. By parallel learning of the main branch and the auxiliary information branch, additional supervisory information is introduced to provide the model with richer contextual information. In the feature fusion stage, we combine ASFF and FPN to integrate features from different scales adaptively, enhancing the model's robustness to variations in container number sizes. Simultaneously, we propose a dual-loss strategy that combines the BCE loss function and the Dice loss function. Blending these two loss functions with weights addresses classification problems and the imbalance between positive and negative samples. DAMNet combines multi-scale feature extraction, an auxiliary information branch, and an improved loss function, demonstrating outstanding performance and stability in container number detection in natural scenes.  
While DAMNet demonstrates outstanding performance and stability in container number detection in natural scenes, it is not without its limitations. Firstly, the computational complexity of the dual-branch architecture may pose challenges for real-time applications, especially on devices with limited computational resources. Future work could explore methods to optimize the network architecture and reduce computational overhead while maintaining high detection accuracy. Secondly, the performance of DAMNet may degrade in extreme lighting conditions or when the containers are partially occluded. Enhancing the robustness of the model to these variations will be an important direction for future research. This could involve incorporating more sophisticated data augmentation techniques or developing new loss functions more robust to such challenges. For future work, we plan to extend DAMNet to other related tasks, such as container type classification or container position detection, to enhance its utility in real-world applications further. We also aim to explore the integration of DAMNet with other advanced technologies, such  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**ICDAR2015**} & \multicolumn{3}{c}{**CTW1500**} & \multicolumn{3}{c}{**Custom Dataset**} \\ \cline{2-10}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline CTPN & 74.2 & 51.6 & 60.8 & 60.4 & 53.8 & 56.9 & 67.8 & 49.5 & 53.8 \\ EAST & 83.2 & 78.3 & 80.7 & 78.7 & 49.1 & 60.4 & 79.2 & 61.3 & 73.1 \\ PSENet & 86.9 & **84.5** & **85.7** & 84.8 & 79.7 & 82.2 & 89.5 & 82.4 & 83.9 \\ PAN & 84.0 & 81.9 & 82.9 & 86.4 & 81.2 & 83.7 & 86.7 & 80.6 & 82.1 \\ DBNet & 87.5 & 76.2 & 81.5 & 84.8 & 77.5 & 81.0 & 93.5 & 92.6 & 91.4 \\ DBNet++ & **90.1** & 77.2 & 83.1 & **86.7** & **81.3** & **83.9** & **97.3** & 94.1 & 93.5 \\ Ours & 89.7 & 80.1 & 83.5 & 86.2 & 80.6 & 82.7 & 96.7 & **97.2** & **95.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Text detection results using cooperative methods on ICDAR2015, CTW1500, and custom datasets.  
as deep learning-based object tracking, to provide a comprehensive solution for container management in natural scenes.  
Conceptualization, L.Y., C.T. and Y.W.; methodology, L.Y. and C.T.; software, C.T.; validation, L.Y. and C.T.; formal analysis, C.T.; investigation, L.Y. and C.T.; resources, L.Y.; data curation, C.T.; writing--original draft preparation, C.T.; writing--review and editing, L.Y. and Y.W.; visualization, C.T.; supervision, L.Y.; project administration, L.Y. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are available upon request from the corresponding author.  
The authors declare no conflicts of interest.