# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 4 Results
### Ablation Study  
To validate the performance of the model, we conduct ablation experiments and evaluate them on the ICDAR2015 dataset and the custom dataset. We ensured that all the experiments were rigorously standardized and conducted in a well-controlled experimental environment to guarantee the fairness and accuracy of the results. Following a series of rigorous testing and evaluation, the experimental results, as presented in Table 1, intuitively demonstrate the model's performance across various evaluation metrics. As shown in Table 1, the Innovative Backbone significantly improved in the precision across all datasets. For the ICDAR2015 dataset, P increased by 0.8, R increased by 3.5, and F increased by 2.3. For the custom dataset, P increased by 1.7, R increased by 0.6, and F increased by 2.0. This confirms that the integration of the dual-branch and EMA attention mechanisms enhances the model's robustness. Compared to the original model, the Innovative Backbone focuses on crucial textual information while effectively filtering out irrelevant information or noise. This improves the model's adaptability to input variations, enabling it to maintain excellent performance in various scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp**} & **Innovative** & \multirow{2}{*}{**ASFF-FPN**} & **Dual-Loss** & \multicolumn{3}{c}{**ICDAR2015**} & \multicolumn{3}{c}{**Custom Dataset**} \\  & & & **Strategy** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Baseline & & & & 87.5 & 76.2 & 81.5 & 93.5 & 92.6 & 91.4 \\ Baseline+ & ✓ & & & 88.3 & 79.7 & **83.8** & 95.2 & 93.2 & 93.4 \\ Baseline+ & & ✓ & & 88.5 & 78.6 & 83.2 & 96.1 & 95.3 & 92.7 \\ Baseline+ & & & ✓ & 87.6 & 77.3 & 82.2 & 93.8 & 96.6 & 94.5 \\ Baseline+ & ✓ & & & ✓ & 88.2 & **80.2** & 82.3 & 94.6 & 93.8 & 93.8 \\ Baseline+ & ✓ & ✓ & & 89.0 & 78.9 & 83.4 & **97.1** & 94.3 & 91.6 \\ Baseline+ & & ✓ & ✓ & 88.5 & 79.3 & 82.1 & 94.2 & 95.2 & 94.9 \\ Ours & ✓ & ✓ & ✓ & **89.7** & 80.1 & 83.5 & 96.7 & **97.2** & **95.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results with different settings of Innovative Backbone, ASFF-FPN, and Dual-loss Strategy. In Table 1, we investigate the effectiveness of ASFF-FPN. By integrating ASFF with the FPN, we achieve a significant improvement in the model's performance. With the introduction of ASFF, the network's capability to fuse shallow text features with deep features is notably enhanced compared to the original network model. This advancement allows the model to incorporate richer cross-scale semantic information, substantially augmenting its detection capability. In Table 1, we evaluate the effect of the dual-loss strategy. Dice loss exhibits advantages in handling class imbalance issues. Compared to BCE loss, Dice loss takes into account the balance between positive and negative samples during loss calculation, enabling the model to pay greater attention and assign higher weights to minority classes. On the ICDAR2015 dataset, after integrating BCE loss and Dice loss with weight fusion, the F, P, and R were improved by 0.7, 0.1, and 1.1. On the custom dataset, after integrating the BCE loss and Dice loss with weight fusion, the F, P, and R were improved by 3.1, 0.3, and 4.0. To quantify the efficacy of the auxiliary information branch in our proposed method, we conduct an ablation study on the custom dataset by removing the auxiliary branch and relying solely on the main branch for feature extraction and final prediction. As shown in Table 2, the experimental results indicate a notable performance degradation when the auxiliary branch is absent. Specifically, P, R, and F decrease to 95.5%, 95.1%, and 93.2%, respectively. By providing targeted features, the auxiliary branch acts as a guiding mechanism, assisting the main branch in learning more salient characteristics, thereby improving the overall performance of the model.