# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 1 Introduction  
With the rapid advancement of artificial intelligence technology, scene text detection has attracted considerable attention in computer vision. Among these research areas, container number detection is vital to natural scene text detection and is crucial in logistics management, customs supervision, and international trade. Container number detection is a significant application within industrial scene image detection, presenting unique and complex challenges compared to object detection in other industrial scenarios. Container numbers often undergo substantial wear and damage due to exposure to harsh environments, greatly increasing the difficulty of detection. In practical situations, collected container images frequently suffer from issues such as image blurring, light reflection interference, and dirt on the container surface, all of which severely affect the accuracy of container number detection. Furthermore, the character spacing and arrangement of the container numbers are inconsistent and diverse, further complicating the recognition process, as shown in Figure 1.  
In recent years, advances in text detection have expanded technological applications, but deploying this technology for container number detection presents significant challenges. Despite using extensive datasets and advanced models, the intricacies of container numbers, encompassing wear and tear, stain interference, and diverse, often challenging lighting conditions, significantly escalate the difficulty of achieving high-precision detection and recognition. Furthermore, container number detection necessitates overcoming the non-standardized issues of environmental variability and inconsistent label positioning, posing even more stringent demands on the robustness and adaptability of algorithms. To address the various issues in current container number detection techniques, this paper proposes a Dual-Branch Adaptive Multi-Scale Network (DAMNet) based on the Differentiable Binarization (DBNet) [1] text detection network, incorporating a dual-branch feature extraction and multi-scale feature fusion to enhance detection accuracy. Firstly, we design an auxiliary information branch to improve the model's feature learning ability for container number texts. The branch utilizes preprocessed images to extract local features, providing richer information to the model. Both the main and the auxiliary information branches utilize Residual Network 18 (ResNet18) [2]. The main branch incorporates Efficient Multi-Scale Attention (EMA) [3] to enhance feature extraction efficiency and accuracy in container number text detection, strengthening the network's multi-scale feature extraction ability without significantly increasing the model size. An Adaptive Spatial Feature Fusion (ASFF) [4] module is introduced to effectively combine various-scale feature maps, integrating detailed low-level information with high-level semantic information. Fusing spatial weights of feature maps across scales minimizes information loss between them. Finally, to balance positive and negative sample losses, this study integrates the Dice loss [5] in the network model, improving its generalization ability. Extensive testing is conducted on the International Conference on Document Analysis and Recognition 2015 (ICDAR2015) dataset [6] and a self-collected dataset of container numbers to validate the network's effectiveness. The experimental results show a significant enhancement in container number  
Figure 1: Container number images in complex scenarios: (**a**) Complex background; (**b**) Container number abrasion; (**c**) Corrugated structure; (**d**) Vertically arranged container number; (**e**) Door latch bar segmenting container number; (**f**) Nighttime container number acquisition. detection accuracy. This refined algorithm improves the model's robustness and accuracy in container number detection tasks. The subsequent sections of this article are structured as follows. Section 2 reviews the related works.