Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework

• URL: http://arxiv.org/abs/2309.01099v1
• Date: Sun, 3 Sep 2023 06:35:07 GMT
• Title: Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework
• Authors: Zhu Liu, Zihang Chen, Jinyuan Liu, Long Ma, Xin Fan, Risheng Liu
• Abstract summary: We propose a bi-level adversarial framework to promote the robustness of detection in the presence of distinct corruptions. Our scheme remarkably improves 21.96% IOU across a wide array of corruptions and notably promotes 4.97% IOU on the general benchmark.
• Score: 61.34862133870934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainstream methodology to establish the mapping directly. However, these methods are susceptible to the inherent complexities of changing backgrounds and real-world disturbances, leading to unreliable and compromised target estimations. In this work, we propose a bi-level adversarial framework to promote the robustness of detection in the presence of distinct corruptions. We first propose a bi-level optimization formulation to introduce dynamic adversarial learning. Specifically, it is composited by the learnable generation of corruptions to maximize the losses as the lower-level objective and the robustness promotion of detectors as the upper-level one. We also provide a hierarchical reinforced learning strategy to discover the most detrimental corruptions and balance the performance between robustness and accuracy. To better disentangle the corruptions from salient features, we also propose a spatial-frequency interaction network for target detection. Extensive experiments demonstrate our scheme remarkably improves 21.96% IOU across a wide array of corruptions and notably promotes 4.97% IOU on the general benchmark. The source codes are available at https://github.com/LiuZhu-CV/BALISTD.

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