Deep Learning for Perception and Recognition Based on Sensor Data: Methods and Applications, 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 1007
Special Issue Editors
Interests: multimodal perception and recognition; light field processing; computer vision in industrial scenarios
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition and machine learning; interpretable artificial intelligence; computer vision; smart grid
Special Issues, Collections and Topics in MDPI journals
Interests: infrared thermography; temperature measurement; deep learning; vision-based measurement; object detection; information fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of deep learning technology has brought about transformative breakthroughs in interpreting data from a multitude of sensors, revolutionizing perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics, where perception is fundamentally built upon heterogeneous sensor streams from cameras, LiDAR, radar, inertial measurement units (IMUs), and beyond.
Deep learning significantly enhances the accuracy and robustness of sensor-based perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as sensor fusion, vision-based inspection, spectral data analysis, and point cloud processing. By training on large volumes of sensor data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize patterns directly from raw or minimally processed sensor inputs.
As application scenarios grow more complex and sensor data become increasingly diverse (e.g., multimodal, high-dimensional, and streaming), deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with inherently noisy, imbalanced, or limited sensor data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled sensor data; seamlessly integrating information across different sensor scales, dimensions, and modalities; and developing explainable and trustworthy perception and recognition systems for high-risk applications.
This Special Issue seeks to highlight advanced research in deep learning for sensor-based perception and recognition. Submitted papers should clearly present novel contributions, whether in general methodologies or innovative applications, addressing any of the following or related topics:
- Deep Learning for Novel Sensor Modalities
- Multimodal and Cross-Modal Sensor Fusion
- Efficient Deep Learning Models for Edge and Sensor Networks
- Self-Supervised/Semi-Supervised Learning for Sensor Data
- Point Cloud and 3D Range Data Processing
- Time-Series and Signal Processing for Sensors
- Explainable AI (XAI) for Sensor-Based Systems
- Hardware-Software Co-Design for Sensor Intelligence
- Multi-Sensor Perception for Robotics: Vision and Beyond
- Intelligent Decision-Making Based on Sensor-Driven Perception
Dr. Gaochang Wu
Dr. Zizhu Fan
Dr. Dong Pan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- image processing
- visual perception
- multimodal learning
- multimodal fusion
- pattern analysis
- knowledge system
- explainable machine learning
- robot vision
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
