An Intelligent Licence Plate Detection Method Can Make The Travel More Convenient And Efficient. However, Traditional Methods Are Reasonably Effective Under The Specific Circumstances Or Strong Assumptions Only,. Therefore, A Novel Real-time Car Plate Detection Method Based On Improved Yolov3 Has Been Proposed. In Order To Select The More Precise Number Of Candidate Anchor Boxed And Aspect Ratio Dimensions, The Deep Learning Object Detection Algorithm Is Utilized. As Shown In The Experimental Results, The Method Which Is Proposed By This Paper Is Better Than Original Yolov3. However, Good Performing Models Such As YOLOv3 In More General Object Detection And Recognition Tasks Could Be Effectively Transferred To The License Plate Detection Application With A Small Effort In Model Tuning. This Paper Focuses On The Design Of Experiment (DOE) Of Training Parameters In Transferring YOLOv3 Model Design And Optimising The Training Specifically For License Plate Detection Tasks. The Parameters Are Categorised To Reduce The DOE Run Requirements While Gaining Insights On The YOLOv3 Parameter Interactions Other Than Seeking Optimised Train Settings. The Result Shows That The DOE Effectively Improve The YOLOv3 Model To Fit The Vehicle License Plate Detection Task.
