SamLP: A Customized Segment Anything Model for License Plate Detection
Haoxuan Ding Junyu Gao Yuan Yuan Qi Wang
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