Cross-Domain Image Detection Model for Target Localization
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Solution Overview
Problem
Deep convolutional neural networks face challenges in target detection due to significant domain distribution differences between training and test images, leading to poor prediction performance when applied to unfamiliar domains.
Innovation Solution
A cross-domain image detection model is developed, utilizing two network models to convert images from one domain to another and perform target localization, enhancing prediction accuracy by aligning feature distributions between domains through adversarial learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a network model is trained on training set images from domain A, then the model can identify targets in domain A images, but the model performs poorly when predicting test set images from domain B due to domain distribution differences
Solution Approach 1:
The patent introduces a style transfer network as an intermediary component between the input image and the target detection model. This style transfer network converts images from domain B (test domain) to domain A (training domain) style, enabling the detection model trained on domain A to effectively process and detect targets in domain B images. The intermediary style transfer mechanism bridges the domain gap without requiring retraining of the detection model.
Solution Approach 2:
The patent changes the stylistic parameters of the input images through style transfer processing. By modifying appearance characteristics such as color distribution, texture patterns, and illumination properties while preserving structural information, the images are transformed to match the training domain distribution, thereby improving detection accuracy across domains.
2Adaptability or versatility
If adversarial loss function is used to constrain features, then the network model becomes insensitive to feature distribution domains, but it cannot identify the domain source and fails to blur features between training and test images
Solution Approach 1:
The patent segments the target detection system into two independent functional modules: a style transfer network and a target detection network. The style transfer network specifically handles domain adaptation by converting image styles, while the detection network focuses on target identification. This segmentation allows each module to specialize in its function, with the style transfer network addressing domain distribution differences through explicit style conversion rather than relying solely on adversarial training.
Data Source
AI summary
The disclosure provides a target detection method and apparatus, a model training method and apparatus, a device, and a storage medium. The target detection method includes: obtaining a first image; obtaining a second image corresponding to the first image, the second image belonging to a second domain; and obtaining a detection result corresponding to the second image through a cross-domain image detection model, the detection result including target localization information and target class information of a target object, the cross-domain image detection model including a first network model configured to convert an image from a first domain into an image in the second domain, and the second network model configured to perform region localization on the image in the second domain.