# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 4 Results
### Comparisons with Previous Methods  
To validate the advancements of our algorithm, we conduct comparative experiments between our presented method and state-of-the-art general natural scene text detection methods on the ICDAR2015 dataset, CTW1500, and our custom container number dataset, utilizing a standardized evaluation metric. In our experimental design, we employ a methodology of conducting multiple repetitions of experiments to ensure the reliability and stability of the results. We conduct multiple independent experiment runs for each dataset. In each experiment, we use the same model configuration and training parameters, make predictions on the test set, and calculate metrics such as P, R, and F. Some qualitative results are visualized in Figure 5. For each unit, the top is the result of DBNet, the bottom is the result of our method.  
The detection results are shown in Table 4. The experimental results indicate that the network model proposed in this paper consistently ranks in the top three in terms of F, P, and R across the three datasets mentioned. Compared to the baseline model DBNet, our method improved by 2.2% in P and 3.9 % in R and 2.0% in F. These data fully demonstrate the advancement and effectiveness of the algorithm introduced in this paper in natural scene text detection.  
To comprehensively evaluate the proposed method's effectiveness, we compared DAMNet with several state-of-the-art text detection algorithms. Our method performed exceptionally well on all three datasets, particularly achieving the highest F of 95.9% on a custom dataset. This indicates that our method exhibits strong generalizability and is highly suitable for detecting text in various scenarios. Despite the overall strong performance of our method, there is still room for improvement. For example, when handling certain types of text or background conditions present in the ICDAR2015 dataset, PSENet achieves a slightly higher F. Similarly, on the CTW1500 dataset, DBNet++ outperforms our method in F, suggesting there is room for improvement in handling curved or irregularly shaped text. Nonetheless, maintaining a scientific style, our method demonstrated stable and reliable performance across all metrics (P, R, F) on all three datasets. The main differences between DAMNet and existing architectures are as follows: firstly, the introduction of an auxiliary information branch that utilizes Otsu thresholding for extracting local features significantly enhances the model's ability to learn container number text features; secondly, the improvement of the feature pyramid structure through Adaptive Spatial Feature Fusion (ASFF) captures global context while preserving detailed information, thereby boosting the model's robustness.  
Figure 5: Some visualization results of DBNet and our method on text instances of various shapes. (The area detected is filled with red color).  
While other methods achieve better results on some benchmark datasets, it is worth noting that DAMNet has demonstrated significantly superior performance on a custom dataset. This difference can be attributed to several factors. Firstly, there are significant differences between the custom dataset and benchmark datasets in terms of data distribution, annotation quality, and task difficulty. Our custom dataset focuses more on specific application scenarios thus possessing unique challenges and characteristics. The design of DAMNet takes these aspects into full consideration and adopts targeted strategies to address these challenges. Secondly, DAMNet has innovated in model architecture to better adapt to the complexities present in the custom dataset. For instance, we have introduced specific modules to enhance the model's ability to capture particular features and optimized training strategies to improve the model's generalization performance.