Domain-Adaptive Object Detection With Zigzag RGB-Thermal Training
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Solution Overview
Problem
Conventional domain adaptation methods are ineffective in bridging the significant gap between RGB and thermal domains due to distinct sensor characteristics and data representations, hindering effective learning.
Innovation Solution
A domain-adaptive object detection method utilizing zigzag learning, where a student model is trained alternately through thermal and RGB domain training, with teacher models updated using exponential moving averages (EMA) to stabilize learning and bridge the domain gap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional domain adaptation methods are used to minimize discrepancy between labeled source domain and unlabeled target domain, then learning effectiveness is improved in small domain gaps, but adaptation effectiveness deteriorates when domain gap is large (RGB to thermal)
Solution Approach 1:
The patent introduces an intermediate domain (synthetic thermal images generated from RGB images) to bridge the large gap between RGB and real thermal domains. This intermediate domain serves as a mediator that allows gradual adaptation, first learning from the intermediate domain then transferring to the target thermal domain, overcoming the limitation of direct adaptation methods.
Solution Approach 2:
The patent performs preliminary domain adaptation training on an intermediate domain before adapting to the final target domain. By pre-training with synthetic thermal images generated from RGB images, the model prepares for the target domain adaptation, making the overall adaptation process more effective for large domain gaps.
2Adaptability or versatility
If domain adaptation is performed from RGB to thermal domain, then object detection capability is improved in thermal domain, but training stability deteriorates due to substantial domain gap
Solution Approach 1:
The intermediate domain acts as a stable transition bridge, preventing direct and unstable adaptation from RGB to thermal domain. The synthetic thermal images provide a stable intermediate representation that reduces training instability caused by the substantial domain gap between RGB and real thermal images.
Solution Approach 2:
By performing preliminary training on the intermediate domain, the model achieves better initialization and stability before final target domain adaptation. This staged approach prevents training instability that would occur with direct adaptation.
3Productivity
If conventional domain adaptation methods are applied, then adaptation speed is improved for small domain gaps, but adaptation accuracy deteriorates for large domain gaps (RGB to thermal)
Solution Approach 1:
The patent performs preliminary adaptation training on the intermediate domain, which accelerates convergence when adapting to the final thermal domain. This two-stage approach maintains fast adaptation speed while improving accuracy by preparing the model in advance on a bridging domain.
Solution Approach 2:
The intermediate domain serves as a mediator that enables both fast and accurate adaptation. It provides a transition path that maintains adaptation speed while improving accuracy by reducing the effective domain gap through the synthetic thermal representation.
Data Source
AI summary
A domain-adaptive object detection method performed by a computing device, which includes a processor and a memory, may include: acquiring, by the processor, an RGB (red, green, blue) teacher model, a thermal teacher model, and a student model; determining, by the processor, a training iteration of the thermal teacher model as a first value; determining, by the processor, a training iteration of the RGB teacher model as a second value; performing, by the processor, thermal domain training on the thermal teacher model and the student model for a number of iterations corresponding to the first value; and performing, by the processor, RGB domain training on the RGB teacher model and the student model for a number of iterations corresponding to the second value.