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A Single-Target License Plate Detection with Attention

arXiv 2112.12070 ↗First posted 12 Dec 2021cs.CVcited by 0

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Abstract (arXiv)

With the development of deep learning, Neural Network is commonly adopted to the License Plate Detection (LPD) task and achieves much better performance and precision, especially CNN-based networks can achieve state of the art RetinaNet[1]. For a single object detection task such as LPD, modified general object detection would be time-consuming, unable to cope with complex scenarios and a cumbersome weights file that is too hard to deploy on the embedded device.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 12 Dec 2021.

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17 of 31 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  9. 2015Fast R-CNNcited by 2,933
  10. 2018YOLOv3: An Incremental Improvementcited by 2,659
  11. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  12. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
5 more
  1. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  2. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  3. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  4. 2019RetinaFace: Single-stage Dense Face Localisation in the Wildcited by 165
  5. 2020Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentationcited by 98

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