Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
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Knowledge-guided adversarial training improves infrared object detection robustness via thermal radiation modeling
Action Steps
- Integrate thermal radiation modeling into the training process to provide knowledge guidance
- Implement adversarial training to enhance model robustness against common corruptions and adversarial examples
- Evaluate the performance of the knowledge-guided adversarial training method on infrared object detection tasks
Who Needs to Know This
Computer vision engineers and researchers working on object detection tasks can benefit from this approach to improve model robustness, particularly in complex environments with diverse scenarios
Key Insight
💡 Incorporating domain knowledge from thermal radiation modeling can enhance the robustness of infrared object detection models
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💡 Improve infrared object detection robustness with knowledge-guided adversarial training via thermal radiation modeling
Key Takeaways
Knowledge-guided adversarial training improves infrared object detection robustness via thermal radiation modeling
Full Article
Title: Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
Abstract:
arXiv:2603.25170v1 Announce Type: cross Abstract: In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both common corruptions and adversarial examples, leading to potential security risks. To improve the robustness of infrared object detection, current methods mostly adopt a data-driven ideology, which only superficially drives the network to fit the training data without speci
Abstract:
arXiv:2603.25170v1 Announce Type: cross Abstract: In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both common corruptions and adversarial examples, leading to potential security risks. To improve the robustness of infrared object detection, current methods mostly adopt a data-driven ideology, which only superficially drives the network to fit the training data without speci
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