AI-Driven Video and Image Processing for Multi-Sensor Data Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 35
Editor
Interests: multi-sensor data fusion; video analysis; edge AI; intelligent perception
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
AI-driven video and image processing for multi-sensor data fusion is an emerging research direction that has attracted enormous attention in recent years. With the widespread deployment of multiple heterogeneous sensors (e.g., RGB cameras, LiDAR, infrared, depth sensors, radar, and hyperspectral imagers), vast amounts of visual data are being generated in applications such as autonomous driving, intelligent surveillance, robotics, smart healthcare, and remote sensing. Effectively fusing these complementary data streams can significantly improve perception accuracy, robustness, and reliability. Artificial intelligence, particularly deep learning, provides powerful tools for feature extraction, cross-modal alignment, and adaptive fusion. However, several key research challenges remain within this domain, including (but not limited to):
- Cross-modal representation learning and feature alignment;
- Robust fusion under sensor misalignment, noise, or missing modalities;
- Real-time video and image fusion for edge and embedded systems;
- Transformer-based and attention-driven fusion architectures;
- 3D perception and object detection using fused multi-sensor data;
- Generative models and self-supervised learning for data augmentation;
- Federated learning and privacy-preserving multi-sensor fusion;
- Domain adaptation and generalization for heterogeneous sensor inputs;
- Benchmark datasets and evaluation metrics for multi-sensor fusion;
- Applications in autonomous navigation, smart cities, and industrial IoT.
Full-length original technical articles are solicited with novel contributions within the AI-driven multi-sensor fusion domain without restriction. Tutorial and survey papers are also welcome.
Dr. Mingye Ju
Guest Editor
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Keywords
- multi-sensor fusion
- video processing
- image processing
- deep learning
- cross-modal learning
- attention mechanisms
- 3D perception
- real-time systems
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