# Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model
## 1 Introduction  
With the development of convolutional neural networks, deep learning-based object detection algorithms have gradually replaced traditional object detection algorithms. License plate detection is a subtask of object detection, and many generalized object detection algorithms can be directly applied to license plate detection. Classical two-stage target detection algorithms including R-CNN, Fast R-CNN, and Faster R-CNN have high detection accuracy, but the detection speed is not satisfactory, and these methods also have performance limitations when dealing with scenarios such as small-targeted license plates, occluded license plates, and dense license plates. Classical single-stage target detection algorithms include SSD, RetinaNet, YOLO, FCOS, CenterNet, etc. Their advantage lies in the ability to achieve very high detection speeds with partial loss of accuracy, but there are some limitations in dealing with target license plates of different scales and robustness to occluded license plates. Even though YOLOv5 is one of the most advanced target detection algorithms available, its performance depends heavily on the training dataset. Therefore, there is still room for improvement in detecting license plates in the face of complex scenarios such as abnormal weather and tilted license plates. With the continuous development in the field of target detection, the solution to these problems is also one of the focuses of future research.  
The Segment Anything Model (SAM) [1] has garnered significant interest in recent times due to its outstanding performance and has become a foundational model in computer vision. Processed images by SAM provide a solid foundation for downstream tasks, showcasing its immense potential in computer vision. The powerful segmentation and zero-shot transfer capabilities of SAM can enhance the performance of visual tasks. In the field of medical image segmentation, [PERSON] et al. [2], cleverly integrated MedLAM (a limitedannotation framework) with SAM. By annotating only six extreme points in three directions on several templates, the model autonomously recognizes target anatomical areas on all data designated for annotation, significantly reducing the annotation workload in medical image segmentation. For 3D object detection, [PERSON] et al. [3] proposed a SAM-powered BEV processing pipeline to leverage SAM's substantial zero-shot capabilities for zero-shot 3D object detection, confirming SAM's applicability in 3D object detection. [PERSON] et al. [4] introduced an end-to-end SpA-Former by incorporating SAM, recovering shadow-free images from a single shadow image. Unlike traditional methods that require two steps for shadow detection and removal, SpA-Former unifies these steps into one, eliminating the need for a separate shadow detection step and any post-processing refinement steps, effectively integrating shadow detection and removal into a single stage. [PERSON] et al. [5] utilized SAM as a guiding module, combined with the lightweight Mask-to-Matte (M2M) module, to precisely improve mask outputs into alpha masks for target instances, addressing various image matting tasks. Inspired by Matting Anything, deducting detected targets in advance facilitates YOLOv5 in target detection. This approach aims to reduce the false positive and false negative rates of YOLOv5, thereby enhancing detection accuracy and performance.