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Object Detection and Recognition Based on Deep Learning
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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 1012

Special Issue Editors

Dr. Alessia Saggese

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Guest Editor
DIEM, University of Salerno, 84084 Salerno, Italy
Interests: computer vision; cognitive robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Spagnolo

E-Mail Website
Guest Editor
National Research Center of Italy, Lecce, Italy
Interests: object detection and recognition; object re-identification; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Carletti

E-Mail Website
Guest Editor
DIEM, University of Salerno, 84084 Salerno, Italy
Interests: computer vision; cognitive robotics

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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • object detection
  • object recognition
  • thermal image analysis
  • multispectral object analysis
  • applications
  • crowd counting
  • zero-shot detection

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Published Papers (1 paper)

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Research

17 pages, 2953 KiB  
Article
A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks
by Vincenzo Carletti, Antonio Greco, Alessia Saggese and Bruno Vento
Sensors 2024, 24(14), 4519; https://doi.org/10.3390/s24144519 - 12 Jul 2024
Viewed by 714
Abstract
The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a [...] Read more.
The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches. Full article
(This article belongs to the Special Issue Object Detection and Recognition Based on Deep Learning)
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