Architectural Patterns and Performance Implications of AI-Integrated Optoelectronic Systems for Defense Applications

Abstract

Artificial intelligence (AI) is increasingly embedded into optoelectronic (EO) and EO/IR defense systems to enable faster perception, autonomous or human-assisted decision-making, and resilient operation in contested environments. This paper presents an adapted PRISMA-like review and critical synthesis of AI-integrated EO systems for defense applications, with emphasis on system architectures and their performance implications across surveillance, recognition, safety-critical and mission-critical use cases. From an initial pool of approximately 3,700 records screened across major scientific databases, 32 primary studies were selected for in-depth analysis and categorized by sensing modality (EO, IR, hyperspectral, multi-sensor fusion), task type (detection, classification, tracking, anomaly detection), and deployment constraints (edge vs. centralized processing, latency, power, and reliability requirements). Rather than ranking methods by reported accuracy—which is often non-comparable due to heterogeneous datasets and evaluation protocols—this study highlights how architectural choices and operational conditions shape real-world performance, including common failure modes such as weather degradation, occlusion, sensor drift, domain shift, and adversarial interference. The synthesis identifies recurring design patterns (hybrid edge–cloud pipelines, safety-isolated compute domains, and human-autonomy teaming loops) and summarizes research gaps in standardization, certification/verification, robustness, and explainability needed for deployable defense systems.

DOI: 10.61416/ceai.v28i2.10198