Object Detection and Recognition Based on Deep Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 March 2025 | Viewed by 51
Special Issue Editors
Interests: computer vision; cognitive robotics
Special Issues, Collections and Topics in MDPI journals
Interests: object detection and recognition; object re-identification; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, there has been a rapid and successful expansion of computer vision research in several application fields, ranging from intelligent video surveillance and cognitive robotics to automatic inspection and autonomous vehicle driving. Within this contest, object detection and recognition are among those areas that have seen great progress in recent years. The intended use of object detection and recognition is to determine the location of an object of interest in each time instant and the class to which the object belongs.
Deep neural networks (DNNs) have recently emerged as a type of powerful machine-learning model with the ability to learn powerful object representations/models without the need to manually design features. In fact, algorithms for object detection are strictly dependent on acquisition devices (such as RGB cameras, thermal devices, infrared devices, cloud points from lidar, and multi/hyper-spectral devices), as well as the availability of data acquired with that specific sensor type.
The aim of this Special Issue of Sensors is to provide some perspective on object detection and recognition research. It will be dedicated to highlighting both theoretical and practical aspects of object detection; applications requiring objects with detection and recognition algorithms, such as crowd counting, flame and smoke detection, or obstacle detection in both autonomous vehicle driving and smart transportation domains; and zero-shot algorithms for object detection and recognition, e.g., based on pre-trained visual questions.
Dr. Alessia Saggese
Dr. Paolo Spagnolo
Dr. Vincenzo Carletti
Guest Editors
Manuscript Submission Information
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Keywords
- object detection
- object recognition
- thermal image analysis
- multispectral object analysis
- applications
- crowd counting
- zero-shot detection
