Adversarial Domain Adaptation for Construction Zone Segmentation

The patent performs preliminary domain adaptation by aligning feature distributions between source and target domains using adversarial learning before actual annotation. The domain discriminator is trained to distinguish between source and target domain features, and the feature alignment module pre-processes the feature representations to reduce domain gap. This preliminary action enables the model to achieve better performance with fewer annotations in the target domain.

The patent introduces an intermediary feature alignment module that mediates between the source domain pre-trained model and the target domain annotation process. This intermediary component transforms and aligns feature representations from the source domain to the target domain, enabling knowledge transfer without requiring full retraining. The intermediary handles the domain adaptation task, reducing the need for extensive target domain annotations.

The patent segments the domain adaptation process into distinct modular components: a domain discriminator module that learns domain-specific feature distributions, a feature alignment module that transforms features between domains, and an active learning module that selects informative samples. This segmentation allows each component to be trained and optimized independently, reducing the overall complexity of the training process while maintaining high domain adaptability.

The patent implements feedback mechanisms where the domain discriminator provides guidance on domain-specific feature characteristics, and the active learning module uses prediction scores to feedback-select the most informative samples for annotation. This feedback loop continuously refines the feature alignment and domain adaptation process, making the complex training process more manageable through iterative improvement guided by measurable metrics.