The growing complexity of modern aviation systems and the increasing demand for real-time situational awareness have accelerated the adoption of optical surveillance technologies in flight operations and ground safety. This review critically examines the evolution, architecture, and applications of camera-based monitoring systems, both onboard and ground-based, enhanced by artificial intelligence (AI) techniques. It explores the integration of smart cameras, infrared sensors, and airborne image recording systems (AIRS) within AI-powered visual analytics pipelines, enabling the automated detection of faults, behavioural monitoring, and prediction of anomalies. Key deep learning models, including convolutional neural networks (CNNs), YOLO variants, and pose estimation frameworks, are evaluated for their effectiveness in detecting instrument panel alerts, pilot activities, runway intrusions, and UAV threats. This paper further explores the integration of optical data with GPS, IMU, and flight telemetry to facilitate context-aware decision-making and incident reconstruction. Regulatory implications, ethical considerations, and practical deployment challenges are also discussed. By consolidating the current state of research and technological deployment, this review identifies critical gaps. It outlines future directions for advancing optical surveillance systems to ensure safer, more innovative, and more transparent aviation operations.
Joseph Chakravarthi Chavali (Thu,) studied this question.