SamLP：一种面向车牌检测的定制化分割一切模型
SamLP: A Customized Segment Anything Model for License Plate Detection
AI总结：
本文提出SamLP，通过LoRA微调和可提示微调将分割一切模型SAM定制为车牌检测器，在有限数据下取得优异检测性能并展现少样本与零样本学习潜力。
摘要：
随着基础模型的出现，这种新颖的深度学习范式在自然语言处理和计算机视觉领域催生了许多强大的成果。基础模型具有诸多优势，例如出色的特征提取能力、强大的泛化能力、优秀的少样本和零样本学习能力等，这些优势对视觉任务十分有益。作为车辆的唯一身份标识，不同国家和地区的车牌（LP）在样式和外观上各不相同，甚至不同类型的车辆也具有不同的车牌。然而，近年来基于深度学习的车牌检测器主要是在特定数据集上训练的，这些有限的数据集制约了车牌检测器的有效性和鲁棒性。为了缓解数据有限带来的负面影响，本文尝试利用基础模型的优势。我们定制了一个视觉基础模型，即分割一切模型（SAM），用于车牌检测任务，并提出了首个基于视觉基础模型的车牌检测器，命名为SamLP。具体来说，我们设计了一种低秩适配（LoRA）微调策略，向SAM中注入额外参数，并将SAM迁移到车牌检测任务中。随后，我们进一步提出了一个可提示微调步骤，赋予SamLP可提示的分割能力。实验表明，与其他车牌检测器相比，我们提出的SamLP取得了有前景的检测性能。同时，所提出的SamLP具有出色的少样本和零样本学习能力，展示了迁移视觉基础模型的潜力。代码可在 https://github.com/Dinghaoxuan/SamLP 获取。
英文摘要：
With the emergence of foundation model, this novel paradigm of deep learning has encouraged many powerful achievements in natural language processing and computer vision. There are many advantages of foundation model, such as excellent feature extraction power, mighty generalization ability, great few-shot and zero-shot learning capacity, etc. which are beneficial to vision tasks. As the unique identity of vehicle, different countries and regions have diverse license plate (LP) styles and appearances, and even different types of vehicles have different LPs. However, recent deep learning based license plate detectors are mainly trained on specific datasets, and these limited datasets constrain the effectiveness and robustness of LP detectors. To alleviate the negative impact of limited data, an attempt to exploit the advantages of foundation model is implement in this paper. We customize a vision foundation model, i.e. Segment Anything Model (SAM), for LP detection task and propose the first LP detector based on vision foundation model, named SamLP. Specifically, we design a Low-Rank Adaptation (LoRA) fine-tuning strategy to inject extra parameters into SAM and transfer SAM into LP detection task. And then, we further propose a promptable fine-tuning step to provide SamLP with prompatable segmentation capacity. The experiments show that our proposed SamLP achieves promising detection performance compared to other LP detectors. Meanwhile, the proposed SamLP has great few-shot and zero-shot learning ability, which shows the potential of transferring vision foundation model. The code is available at https://github.com/Dinghaoxuan/SamLP
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
