GAN-Based Synthetic Data for Infrared Object Detection
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Solution Overview
Problem
Training object detection models to detect objects in non-visible spectrums, such as infrared, is challenging due to the lack of annotated training data, which is time-consuming and difficult to generate, especially when datasets are limited outside the visible spectrum.
Innovation Solution
The use of generative adversarial networks (GANs) to transform annotated RGB data into synthetic annotated data in different spectrums, like infrared, allowing models trained in the visible spectrum to detect objects in non-visible domains, and refining techniques like gray-scaling and alpha-blending to improve data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If annotated training data is generated for non-visible spectrums, then object detection capability in infrared spectrum is improved, but time consumption and difficulty of data generation increase
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to generate synthetic annotated training data in the infrared spectrum by copying and transforming patterns from annotated visible spectrum images. The generator creates realistic infrared images with corresponding annotations, eliminating the need for manual annotation of infrared datasets while preserving the statistical properties and object distributions of real infrared data.
Solution Approach 2:
The system performs preliminary data preparation by pre-training the GAN model on available annotated visible spectrum data before deploying it for infrared object detection. This preliminary action creates a ready-to-use synthetic infrared dataset that can be immediately applied to train object detection models, avoiding the time-consuming process of collecting and annotating real infrared images later.
2Measurement precision
If large datasets are collected for non-visible domains, then detection accuracy is improved, but data collection and annotation difficulty increase
Solution Approach 1:
The GAN-based system copies the statistical characteristics, object distributions, and annotation structures from readily available annotated visible spectrum datasets to generate synthetic infrared training data. This approach achieves detection accuracy comparable to models trained on large real infrared datasets without the burden of collecting and annotating actual infrared images.
Solution Approach 2:
The system transforms training data from the visible spectrum domain to the infrared spectrum domain by changing the spectral parameters through the GAN transformation. This parameter change allows models trained on easy-to-obtain visible spectrum data to achieve accurate detection in the infrared spectrum, bypassing the need to collect infrared-specific data.
3Object-affected harmful factors
If feature data transformation is performed, then privacy protection is improved, but computational intensity increases
Solution Approach 1:
The system extracts and transforms only the essential feature representations from images rather than processing complete high-resolution images. By working with compressed feature vectors and latent representations generated by the GAN, the system achieves privacy protection while reducing the computational burden compared to processing full-resolution images through multiple transformation stages.
Data Source
AI summary
Techniques are generally described for object detection in image data. A first frame of image data associated with a first domain is received by a detector executing on at least one computing device. The detector generates a first feature data in the first domain. The first feature data is transformed from the first domain into a second feature data in a second domain. The detector may be effective to detect objects in the second domain. A location of an object in the first frame of image data is determined based at least in part on the second feature data.