Traffic Sign Image Manipulation for Altered Sign Training Data
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Solution Overview
Problem
Existing systems lack efficient methods to simulate physical changes in traffic signs, such as vegetation, graffiti, bending, or corrosion, without physically traveling to altered signs, which is crucial for training machine-learning algorithms for object recognition.
Innovation Solution
A computer system generates artificial images of traffic signs with simulated physical changes by applying manipulations like vegetation, graffiti, bending, or corrosion to identified traffic signs in initial images, using bounding boxes and various pixel operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical inspection of altered traffic signs is performed, then training data for machine-learning algorithms can be obtained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent creates artificial copies of traffic signs by applying simulated physical changes (vegetation, graffiti, bending, corrosion) to original traffic sign images. These synthetic copies serve as training data, eliminating the need to physically travel to and photograph actual altered signs, thus dramatically reducing time consumption while maintaining training data quality
Solution Approach 2:
The system pre-generates a comprehensive dataset of traffic signs with various physical changes applied through digital manipulation. This preliminary action creates a ready-to-use training corpus before actual machine-learning model training begins, avoiding the need for time-consuming field inspections during the training process
2Measurement precision
If physical travel to altered traffic signs is required, then realistic training images can be captured, but resource efficiency and productivity deteriorate
Solution Approach 1:
The patent replaces the mechanical process of physically traveling to, photographing, and collecting altered traffic signs with a computational system that digitally manipulates original images to simulate physical changes. This substitution maintains image realism through sophisticated pixel-level operations while dramatically improving operational efficiency
Solution Approach 2:
The system introduces an intermediary computational layer that processes original traffic sign images through simulated physical change algorithms. This intermediary generates realistic-looking altered sign images without requiring physical interaction with actual altered signs, thus maintaining image quality while improving productivity
3Adaptability or versatility
If diverse physical changes are simulated, then machine-learning algorithm robustness improves, but system complexity increases
Solution Approach 1:
The patent segments the simulation process into distinct modular operations: vegetation overlay, graffiti application, bending transformation, and corrosion effect. Each physical change type is handled by a separate computational module, making the system more manageable and easier to implement despite the diversity of changes being simulated
Solution Approach 2:
The system achieves diverse physical changes by manipulating image parameters (pixel values, geometric transformations, color adjustments) rather than requiring complex physical modeling. By changing parameters of existing images rather than creating entirely new complex models, the system maintains robustness while controlling complexity
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a plurality of initial images including traffic signs, identify the traffic signs in the initial images, and generate new images by applying manipulations to the traffic signs in the initial images. The manipulations simulate physical changes to the traffic signs.