License Plate Defacement Classification Using Neural Network Region Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current license plate recognition technologies face inefficiencies in recognizing defaced plates, as they typically process entire vehicle images, with the license plate being a small proportion, leading to reduced recognition accuracy and efficiency.
Innovation Solution
A method for generating a license plate defacement classification model using annotated vehicle images with location information, employing a neural network trained with cross-entropy and class activation map loss functions to predict whether a license plate is defaced, focusing on the license plate area and ignoring background features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire vehicle image is used as input for license plate recognition, then the model can process the complete scene context, but the license plate occupies only a small proportion leading to reduced recognition accuracy and efficiency
Solution Approach 1:
The patent applies segmentation by dividing the vehicle image processing into two stages: first identifying the license plate region, then focusing recognition on that specific segment. The neural network is trained to locate and segment the license plate area from the entire vehicle image, converting a full-image classification problem into a targeted region-based problem, thereby improving both accuracy and efficiency.
Solution Approach 2:
The patent extracts the license plate region from the entire vehicle image by training the neural network to identify and isolate the license plate area. This extraction process removes irrelevant background information and focuses computational resources on the critical license plate region, resolving the contradiction between processing complete scene context and achieving high recognition accuracy.
2Reliability
If the neural network processes the entire vehicle image, then all visual information is available, but background features interfere with license plate defacement detection
Solution Approach 1:
The patent extracts only the license plate region from the vehicle image for defacement detection, removing background features that could interfere with detection. By isolating the license plate area, the model eliminates harmful interference from non-relevant image factors while maintaining reliable detection performance.
Solution Approach 2:
The patent applies local quality by applying different processing strategies to different regions: the license plate region receives focused attention with specialized defacement detection, while background regions are excluded or given minimal processing. This localized approach improves reliability by concentrating computational resources on the critical area while eliminating interference from other regions.
Data Source
AI summary
A method for generating a license plate defacement classification model, a license plate defacement classification method an electronic device and a storage medium, and related to the technical field of artificial intelligence, and specifically, to the technical field of computer vision and the technical field of intelligent transportation are provided. The method for generating a license plate defacement classification model includes: acquiring training data, wherein the training data includes a plurality of annotated vehicle images, annotated content includes information indicating that a license plate is defaced or is not defaced, and the annotated content further includes location information of a license plate area; and training a first neural network by using the training data, to obtain the license plate defacement classification model for predicting whether the license plate in a target vehicle image is defaced. A robust license plate defacement classification model can be obtained by using embodiments.