At the IEEE Access journal, a new systematic review entitled “The Rise of UAV-Based Smart Surveillance: A Systematic Review of Trends and Technologies,” examines the rapid evolution of unmanned aerial vehicles (UAVs) in surveillance, highlighting their growing role in both civilian and military domains.
The authors — Sudesh Kumar, Anshuman Tiwari, Yogesh Ahirwar, Gaurav Kumar, and Muhammad Yeasir Arafat — framed UAVs as “advanced aerial platform[s] that operate autonomously,” capable of executing missions “under operator control through distant commands or automatically through programmed software and artificial intelligence (AI).”
The review traced the transformation of UAVs from military reconnaissance tools into versatile assets for public safety, infrastructure monitoring, and disaster response. “UAVs serve as advanced instruments in various fields,” the authors wrote, “because they can effectively operate through rugged terrains and risky spots and execute tasks with high precision.” Their integration into smart cities, border control, and emergency management systems has made them “essential tools for modern surveillance systems.”
The paper’s methodology followed PRISMA guidelines, drawing from Scopus, IEEE Xplore, Web of Science, and Science Direct. From an initial pool of 170 articles published between January 2020 and March 2025, the authors screened and selected 97 for in-depth analysis. The review aimed to answer four core research questions, including: “What is the present market landscape of UAV-based surveillance?” and “What key challenges impact the deployment of UAV surveillance systems particularly the computational constraint on edge UAVs?”
The global UAV surveillance market was valued at $14.14 billion in 2023 and was projected to grow to $47.16 billion by 2032, “advancing at a compound annual growth rate (CAGR) of 13.15%.” The authors attributed this growth to “the rising need for cost-effective and scalable real-time monitoring solutions.” North America led the market with more than 35% of the share, driven by “substantial defense expenditure, smart city initiatives, and strict security policies.” Meanwhile, Asia-Pacific nations like India and China were “implement[ing] UAVs for urban development, emergency management, and border protection.”
The review categorized UAVs into fixed-wing, rotor-wing, hybrid, lighter-than-air, and flapping-wing types. Fixed-wing UAVs offer “reliable operational capacity and extended operational range,” making them suitable for “agricultural mapping applications, border monitoring, and powerline inspection operations.” Rotor-wing UAVs, including single and multi-rotor designs, were praised for their “vertical take-off and landing (VTOL) capabilities” and “hovering ability,” though they suffered from “higher energy consumption.”
Hybrid UAVs combined the strengths of both fixed- and rotor-wing platforms. Tilt-rotor and quad-plane designs enabled “vertical take-off with long-range capability,” but introduced “mechanical complexity” and “higher cost.” Lighter-than-air UAVs, such as blimps and balloons, were “extremely quiet” and “cost-effective,” but struggled with “very slow” speeds and “sensitivity to wind.” Flapping-wing UAVs, or ornithopters, mimicked bird or insect flight and were “ideal for covert operations,” though limited by “complex mechanics” and “limited payload.”
Technological advancements were central to the review. Lightweight AI models running directly on drones allowed UAVs to “identify, classify, and respond in real time without transmitting raw video to the cloud.” This reduced “latency, bandwidth requirements, and mitigated privacy risks.” The integration of 5G, mesh networking, and low-latency radio links enabled “continuous telemetry, live analytics offloading, and multi-platform coordination.”
Sensor technologies also advanced rapidly. Compact, high-resolution payloads — including “electromagnetic (visible) and infrared (thermal) sensors, multispectral and hyperspectral cameras, light detection and ranging (LiDAR) modules, and long-range zoom optics” — were now available on commercial platforms like the DJI Enterprise series.
The review spotlighted several AI-driven surveillance models. UAV-AdNet used deep neural networks for “unsupervised anomaly detection,” while SiamAPN++ offered “a precise balance between tracking accuracy and computational speed.” Another system integrated YOLOv3-SPP with deep SORT for “effective multi-UAV tracking … appropriate for urban deployment.” The authors also highlighted a “vision transformer (ViT)” model that enhanced “tracking resilience in a variety of dynamic situations.”
Swarm coordination has emerged as a frontier challenge. A hybrid-AI architecture using deep reinforcement learning and a centralized mission planner enabled “coordinated, adaptive, and efficient” ground-target tracking. Meanwhile, the optical flow-assisted graph-pooling residual network (OF-GPRN) improved “UAV recognition in dual-vision surveillance systems,” especially during “difficult day-to-night transitions.”
Security was another focal point. A proposed quantum key distribution (QKD) mechanism for UAV-to-ground-station communication aimed to guarantee “that conversations are shielded from possible cyber threats and eavesdropping.”
The authors concluded that UAVs have become “essential components of current surveillance systems,” offering “real-time, high-resolution surveillance across challenging terrains.” However, they cautioned that issues of “privacy, security, and safe operation” remained unresolved, particularly in the context of drone swarms and autonomous decision-making.
By bridging academic research and industry practice, the review provided a roadmap for future innovation in “intelligent, reliable, and scalable UAV-based surveillance systems.”
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