Sensor-Fusion-Based Deep Interpretable Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 15 November 2024 | Viewed by 590
Special Issue Editors
Interests: visual information processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; machine learning; pattern recognition; computer vision; biomedical imaging and multimedia applications
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: image and video semantic segmentation; deep learning; industrial process control; industrial intelligence; natural language processing; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sensor fusion is a technology that combines data and information from multiple sensors to obtain more comprehensive, accurate, and reliable perception results. Sensor fusion based on deep interpretable networks is more advanced, utilizing the powerful modeling and abstraction capabilities of deep learning to process multi-source sensor data, while emphasizing the interpretability of the model, making the output results of the model more convincing and credible.
In this network, data fusion technology can make full use of redundant information and complementarity between different sensors to improve the overall perception accuracy and robustness. By accurately calibrating and synchronizing various sensors, deep learning models can learn deeper features and complex patterns in the fused data. In addition, the interpretability of the model aids in understanding its internal working principles and decision making, which is crucial for the safety and reliability of key application scenarios, such as autonomous driving and intelligent manufacturing.
This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of sensor fusion-based deep interpretable networks.
Potential topics include but are not limited to the following:
- Sensor Fusion for Comprehensive Perception;
- Multi-View Adaptive Fusion Network for Object Detection, Recognition and Understanding;
- Deep Learning and Sensor Fusion for Enhanced Decision Making;
- Open-Source Sensor Fusion for different application scenarios;
- Kalman and Complementary Filters in Sensor Fusion;
- Autonomous Driving with Scene Understanding;
- Sensor Fusion for Motion Tracking Capabilities in Smartphones and Tablets.
Dr. Guangfeng Lin
Prof. Dr. Guoliang Fan
Dr. Zhigang Ling
Dr. Jiangyun Li
Guest Editors
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Keywords
- sensors fusion
- reliability networks
- causal reasoning
- interpretable networks
- graph neural networks
- attention fusion mechanism
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