# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 2 Related Works
### Text Detection in Container Number  
Text detection in natural scenes is a well-established research field, and container number images are considered a subset of natural scene images. Compared to other text detection datasets in natural scenes, containers are exposed to external environments for extended periods, presenting considerable challenges for accurate container number detection. Container number detection can be categorized into traditional methods and deep learning-based methods. [PERSON] et al. [13] proposed a container number localization method using mathematical morphology techniques like dilation and erosion, which enhances morphological structuring elements for localization. However, this method struggles with complex backgrounds and intricate parameter tuning. [PERSON] et al. [14] introduced a technique that detects container numbers based on character edge features. Yet, this method is sensitive to image noise and lighting conditions and encounters challenges in handling character adhesion issues. [PERSON] et al. [15] employed the Maximally Stable Extremal Regions (MSERs) [16] method for container number localization, but its high computational complexity limits practicality. [PERSON] et al. [17] introduced an algorithm that utilizes horizontal high-pass filters and line-scanning analysis to locate image container numbers, but it may be affected by noise and interference enhancement from the high-pass filters. In recent years, many two-stage algorithms for container number detection have incorporated deep learning techniques. [PERSON] et al. [18] proposed a container number localization model based on You Only Look Once version 3 (YOLOV3) [19], known for its object detection capabilities but requires a balance between precision and efficiency for text detection. [PERSON] et al. [20] introduced a method using the Efficient and Accurate Scene Text Detector (EAST) [21] text detection network for container number region localization and a fully convolutional network for character recognition. However, the EAST model may struggle with highly skewed or distorted container numbers. [PERSON] et al. [22] achieved container code detection based on the YOLOv8 [23] model. Initially, a custom YOLOv8 model was employed to preliminarily locate the regions containing the container codes and crop them. To enhance character detection accuracy, the model treated each character as an individual object for detection. The detected characters were then integrated into complete container codes through a post-processing module for validation. However, for container texts with poor image quality or uneven character distribution, the detection performance of this method may be somewhat lacking. The method proposed by [PERSON] et al. [24] initially removed noise through edge detection to locate the character-line regions. Subsequently, it employed the Histogram of Oriented Gradients (HOGs) [25] features and sliding window techniques, combined with non-maximal suppression, to detect characters at multiple scales. However, this method may exhibit suboptimal performance when handling complex backgrounds and character deformations. [PERSON] et al. [26] utilized the ResNet and Convolutional Networks for Biomedical Image Segmentation (U-Net) [27] models for container code localization. Their approach involved processing RGB rear-view images of container codes to accurately locate the codes and crop the corresponding regions. However, challenges such as complex environmental backgrounds and poor lighting conditions may result in imprecise cropping of image regions. Given the relatively large number of parameters required when stacking two deep neural networks, some researchers have proposed using one-stage methods. [PERSON] et al. [28] presented a one-stage method based on Transformers, constructing a pixel-level character region adaptive network based on Transformers to extract the container number regions of interest from the preselected areas accurately, but it demands substantial data and computational resources.