{"type":"ScholarlyArticle","title":"A Dataset and Model for Realistic License Plate Deblurring","authors":[{"name":"Haoyan Gong","slug":"haoyan-gong","given":"Haoyan","family":"Gong","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Yuzheng Feng","slug":"yuzheng-feng","given":"Yuzheng","family":"Feng","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Zhenrong Zhang","slug":"zhenrong-zhang","given":"Zhenrong","family":"Zhang","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Xianxu Hou","slug":"xianxu-hou","given":"Xianxu","family":"Hou","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Jingxin Liu","slug":"jingxin-liu","given":"Jingxin","family":"Liu","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Siqi Huang","slug":"siqi-huang","given":"Siqi","family":"Huang","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"},{"name":"Hongbin Liu","slug":"hongbin-liu","given":"Hongbin","family":"Liu","affiliation":"School of AI and Advanced Computing , Xi'an Jiaotong-Liverpool University"}],"abstract":"Vehicle license plate recognition is a crucial task in intelligent traffic management systems. However, the challenge of achieving accurate recognition persists due to motion blur from fast-moving vehicles. Despite the widespread use of image synthesis approaches in existing deblurring and recognition algorithms, their effectiveness in real-world scenarios remains unproven. To address this, we introduce the first large-scale license plate deblurring dataset named License Plate Blur (LPBlur), captured by a dual-camera system and processed through a post-processing pipeline to avoid misalignment issues. Then, we propose a License Plate Deblurring Generative Adversarial Network (LPDGAN) to tackle the license plate deblurring: 1) a Feature Fusion Module to integrate multi-scale latent codes; 2) a Text Reconstruction Module to restore structure through textual modality; 3) a Partition Discriminator Module to enhance the model's perception of details in each letter. Extensive experiments validate the reliability of the LPBlur dataset for both model training and testing, showcasing that our proposed model outperforms other state-of-the-art motion deblurring methods in realistic license plate deblurring scenarios. The dataset and code are available at https://github.com/haoyGONG/LPDGAN.","doi":"10.24963/ijcai.2024/86","url":"https://ijcai.org/proceedings/2024/86","pdf":"https://ijcai.org/proceedings/2024/0086.pdf","markdown":"https://ijcai.org/proceedings/2024/86.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/86","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":86,"pages":{"first":776,"last":784},"keywords":["Computer Vision: CV: Adversarial learning, adversarial attack and defense methods","Computer Vision: CV: Applications","Computer Vision: CV: Image and video synthesis and generation"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2021] Meng Chang, Chenwei Yang, Huajun Feng, Zhihai Xu, and Qi Li. 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