Multihop Anchor-Free Network With Tolerance-Adjustable Measure for Infrared Tiny Target Detection

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Abstract
Infrared tiny target detection is faced with difficulty that tiny-sized targets are easily drowned out in complex backgrounds. To address this problem, this paper presents an end-to-end deep learning framework, which consists of an anchor-free U-shaped backbone for extracting image features and a detection head block for target attributes regression. To capture fine-grained details and semantic information, a multi-hop and multiscale fusion block is designed in the backbone for effective progressive feature fusion. Moreover, to adaptively adjust the constraints for the localization and shape description of the targets, a tolerance-adjustable measure is proposed in the detection head to provide a reliable means for determining the authenticity of targets. Extensive experiments indicate that our trained model can simultaneously detect infrared targets with various sizes, especially achieve an excellent detection performance for point targets with 98.2% F1-score, 3.1% error rate and 0.5% omission rate. Comparative experiments indicate that the proposed method is superior to SOTA deep learning based methods, especially for tiny targets.
Funding Information
  • Space Optoelectronic Measurement and Perception Laboratory, Beijing Institute of Control Engineering (LabSOMP-2024-06)
  • National Natural Science Foundation of China (62171142)
  • National Natural Science Foundation of Guangdong Province, China (2021A1515011908)
  • Project of Science and Technology, Maoming, China (2022S048)

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