LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification

• URL: http://arxiv.org/abs/2604.08741v1
• Date: Thu, 09 Apr 2026 20:07:36 GMT
• Title: LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification
• Abstract summary: Large-scale real-world usage remains challenging due to low-quality imaging devices, compression artifacts, and suboptimal camera installation.<n>In this work, we expand the original benchmark to over three times the size with two extra capture days, revise its annotations and introduce novel labels.<n>We present a novel training procedure featuring an Exponential Moving Average-based loss function and a refined learning rate scheduler, addressing common mistakes in testing.
• Score: 2.0215972783560745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern Automatic License Plate Recognition (ALPR) systems achieve outstanding performance in controlled, well-defined scenarios. However, large-scale real-world usage remains challenging due to low-quality imaging devices, compression artifacts, and suboptimal camera installation. Identifying illegible license plates (LPs) has recently become feasible through a dedicated benchmark; however, its impact has been limited by its small size and annotation errors. In this work, we expand the original benchmark to over three times the size with two extra capture days, revise its annotations and introduce novel labels. LP-level annotations include bounding boxes, text, and legibility level, while vehicle-level annotations comprise make, model, type, and color. Image-level annotations feature camera identity, capture conditions (e.g., rain and faulty cameras), acquisition time, and day ID. We present a novel training procedure featuring an Exponential Moving Average-based loss function and a refined learning rate scheduler, addressing common mistakes in testing. These improvements enable a baseline model to achieve an 89.5% F1-score on the test set, considerably surpassing the previous state of the art. We further introduce a novel protocol to explicitly addresses camera contamination between training and evaluation splits, where results show a small impact. Dataset and code are publicly available at https://github.com/lmlwojcik/LPLCv2-Dataset.

Related papers

• Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data [0.0]
  We present a label-free approach that scores clips by the prediction-error "novelty" of a self-supervised joint-embedding predictive architecture (JEPA)<n>We show that this apparent success is actually a domain-shift consequence: on a fair benchmark drawn from a single dataset, this mechanism collapses to chance and is on par with simple no-training baselines.<n>We present this as a study for evaluating the effectiveness of self-supervised learning, where cross-dataset protocols can silently reward domain separation over novelty.
  arXiv  Detail & Related papers  (2026-08-02T15:57:40Z)
• Efficient License Plate Recognition via Pseudo-Labeled Supervision with Grounding DINO and YOLOv8 [0.29949629644252374]
  ALPR is vital in traffic control, parking, vehicle tracking, toll collection, and law enforcement applications.<n>This paper proposes a deep learning strategy using YOLOv8 for license plate detection and recognition tasks.<n>It achieved an impressive recall rate of 94% on the dataset from the Center for Pattern Recognition and Machine Intelligence (CENPARMI) and 91% on the UFPR-ALPR dataset.
  arXiv  Detail & Related papers  (2025-10-28T23:21:00Z)
• LPLC: A Dataset for License Plate Legibility Classification [1.907858714149189]
  We introduce a novel dataset comprising 10,210 images of vehicles with 12,687 annotated LPs for legibility classification.<n>The images span a wide range of vehicle types, lighting conditions, and camera/image quality levels.<n>We propose a classification task using three image recognition networks to determine whether an LP image is good enough, requires super-resolution, or is completely unrecoverable.
  arXiv  Detail & Related papers  (2025-08-25T19:18:02Z)
• Weakly Supervised Semantic Segmentation for Driving Scenes [27.0285166404621]
  State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) exhibit severe performance degradation on driving scene datasets. We develop a new WSSS framework tailored to driving scene datasets.
  arXiv  Detail & Related papers  (2023-12-21T08:16:26Z)
• An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates [1.5208105446192792]
  This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and Normalizing flows. The model uses two new strategies. Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters.
  arXiv  Detail & Related papers  (2022-07-21T22:22:57Z)
• Video-based Person Re-identification without Bells and Whistles [49.51670583977911]
  Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. There exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. We present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets.
  arXiv  Detail & Related papers  (2021-05-22T10:17:38Z)
• A Closer Look at Temporal Sentence Grounding in Videos: Datasets and Metrics [70.45937234489044]
  We re- organize two widely-used TSGV datasets (Charades-STA and ActivityNet Captions) to make it different from the training split. We introduce a new evaluation metric "dR@$n$,IoU@$m$" to calibrate the basic IoU scores. All the results demonstrate that the re-organized datasets and new metric can better monitor the progress in TSGV.
  arXiv  Detail & Related papers  (2021-01-22T09:59:30Z)
• Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos [82.02074241700728]
  In this paper, we present a prohibitive-level action recognition model that is trained with only video-frame labels. Our method per person detectors have been trained on large image datasets within Multiple Instance Learning framework. We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid.
  arXiv  Detail & Related papers  (2020-07-21T10:45:05Z)
• A Robust Attentional Framework for License Plate Recognition in the Wild [95.7296788722492]
  We propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. We release a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China.
  arXiv  Detail & Related papers  (2020-06-06T17:11:52Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.