Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges

2025-07-14

AI Summary

Addressing critical challenges in visual anti-drone tracking—namely low detection accuracy, poor robustness, and insufficient multi-source data fusion under complex scenarios—this paper presents a systematic review of state-of-the-art vision-based drone detection and tracking techniques. It synthesizes major public benchmarks and multi-sensor fusion approaches (visible-light, infrared, and RF) while analyzing performance bottlenecks in occlusion, small-object tracking, and dynamic backgrounds. Methodologically, the work proposes a practice-oriented research framework for visual anti-drone tracking, structured around four pillars: (1) task-specific dataset construction, (2) model lightweighting, (3) cross-modal feature alignment, and (4) real-time optimization. The contribution includes a comprehensive, theory-grounded yet engineering-practical reference that identifies key future directions: interpretable modeling, weakly supervised learning, and edge-cooperative tracking—thereby bridging algorithmic innovation with operational deployment requirements.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the rapid advancement of UAV technology and its extensive application in various fields such as military reconnaissance, environmental monitoring, and logistics, achieving efficient and accurate Anti-UAV tracking has become essential. The importance of Anti-UAV tracking is increasingly prominent, especially in scenarios such as public safety, border patrol, search and rescue, and agricultural monitoring, where operations in complex environments can provide enhanced security. Current mainstream Anti-UAV tracking technologies are primarily centered around computer vision techniques, particularly those that integrate multi-sensor data fusion with advanced detection and tracking algorithms. This paper first reviews the characteristics and current challenges of Anti-UAV detection and tracking technologies. Next, it investigates and compiles several publicly available datasets, providing accessible links to support researchers in efficiently addressing related challenges. Furthermore, the paper analyzes the major vision-based and vision-fusion-based Anti-UAV detection and tracking algorithms proposed in recent years. Finally, based on the above research, this paper outlines future research directions, aiming to provide valuable insights for advancing the field.

Problem

• Achieving efficient and accurate Anti-UAV tracking in complex environments
• Reviewing challenges in vision-based Anti-UAV detection and tracking
• Analyzing vision-fusion algorithms for improved Anti-UAV security

Innovation

• Uses computer vision for Anti-UAV tracking
• Integrates multi-sensor data fusion
• Analyzes vision-based detection algorithms