Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method

Haoxiang Zhang, Chao Liu (), Jianguang Ma and Hui Sun
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Haoxiang Zhang: School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610095, China
Chao Liu: Institute of Systems Engineering, Academy of Military Sciences (AMS), People’s Liberation Army of China (PLA), Beijing 100091, China
Jianguang Ma: Institute of Systems Engineering, Academy of Military Sciences (AMS), People’s Liberation Army of China (PLA), Beijing 100091, China
Hui Sun: Institute of Systems Engineering, Academy of Military Sciences (AMS), People’s Liberation Army of China (PLA), Beijing 100091, China

Mathematics, 2024, vol. 12, issue 1, 1-20

Abstract: Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS).

Keywords: ship infrared automatic target recognition; deep learning; bipartite graph model recommendation; simulate infrared data of a ship (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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