Synthetic 3D Image Training Data for Power Grid Equipment Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image recognition models for power grid equipment face challenges due to the lack of diverse and realistic training datasets, leading to low-confidence models and increased false positives, as current synthetic scene generation techniques fail to replicate real-world attributes.
Innovation Solution
A method involving LiDAR and photogrammetry to capture dimensionally accurate 3D models of power grid equipment, which are then inserted into real-world scenes using image editing software to create photorealistic synthetic images, enhancing the training dataset with varied conditions and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-world images are collected for training datasets, then the training data diversity increases, but the time and effort required to obtain sufficient images across various scenarios increases significantly
Solution Approach 1:
The patent uses photogrammetry to create accurate 3D digital copies of power grid equipment from real-world photographs. These 3D models can then be virtually instantiated in numerous synthetic training images across different scenarios, environments, and conditions without requiring physical collection of images in each scenario. This copying approach enables diverse training data generation while eliminating the time-consuming field collection process.
2Quantity of substance
If synthetic scenes are generated using game engines, then the training dataset size increases, but the realism and accuracy of real-world attribute replication decreases
Solution Approach 1:
The patent replaces traditional game engine rendering with a photogrammetry-based synthesis approach. Instead of using artificial lighting and material models in game engines, the system captures actual photometric properties from real photographs and applies them to 3D models. This substitution of the image generation mechanism preserves authentic real-world attributes like lighting characteristics, shadows, and surface textures while still enabling synthetic scene generation.
3Measurement precision
If 3D models are created using LiDAR and photogrammetry, then the accuracy of equipment representation improves, but the complexity of the data processing pipeline increases
Solution Approach 1:
The patent merges LiDAR point cloud data with photogrammetry-derived texture and color information into a unified 3D model representation. By combining these two data sources, the system achieves both geometric accuracy from LiDAR and photorealistic surface properties from photogrammetry, creating comprehensive equipment models that require processing only once rather than maintaining separate data pipelines.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the accuracy and performance of image recognition models by providing high-quality, diverse training data that mimics real-world scenarios, reducing false positives and enhancing model confidence.
Implementation Method 1
A process begins with accessing a database of real-world 3-D images of equipment in a power grid, the 3-D images of equipment include 3-D measurements to create a dimensionally accurate and photorealistic model of the equipment
Implementation Method 2
A method involving LiDAR and photogrammetry to capture dimensionally accurate 3D models of power grid equipment
Data Source
AI summary
Systems and methods to generate synthetic images for use in a machine learning training set. The process begins with accessing a database of real-world 3-D images of equipment in a power grid, the 3-D images of equipment include 3-D measurements to create a dimensionally accurate and photorealistic model of the equipment. Optionally, the 3-D images could be aged or weathered using imaging editing software. Next, a database of real-world photographs of scenes in which the equipment is installed is accessed. Optionally, the identical scenes can be captured at different times of day, different times of the year, and at different perspectives. Next, using image editing software, the 3-D images of equipment is inserted into at least one of the scenes to form a synthetic image based on a combination of the equipment and the scene in which each of the equipment and the scene were previously captured independently of each other.