Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Object Detection and Recognition Based on Deep Learning
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 10 December 2025 | Viewed by 6775

Special Issue Editors

Dr. Alessia Saggese

E-Mail Website
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
Institute of Applied Science and Intelligent Systems, National Research Council, 73100 Lecce, Italy
Interests: artificial intelligence; computer vision; pattern recognition; 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

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 18399 KiB  
Article
Channel Attention for Fire and Smoke Detection: Impact of Augmentation, Color Spaces, and Adversarial Attacks
by Usama Ejaz, Muhammad Ali Hamza and Hyun-chul Kim
Sensors 2025, 25(4), 1140; https://doi.org/10.3390/s25041140 - 13 Feb 2025
Viewed by 644
Abstract
The prevalence of wildfires presents significant challenges for fire detection systems, particularly in differentiating fire from complex backgrounds and maintaining detection reliability under diverse environmental conditions. It is crucial to address these challenges for developing sustainable and effective fire detection systems. In this [...] Read more.
The prevalence of wildfires presents significant challenges for fire detection systems, particularly in differentiating fire from complex backgrounds and maintaining detection reliability under diverse environmental conditions. It is crucial to address these challenges for developing sustainable and effective fire detection systems. In this paper: (i) we introduce a channel-wise attention-based architecture, achieving 95% accuracy and demonstrating an improved focus on flame-specific features critical for distinguishing fire in complex backgrounds. Through ablation studies, we demonstrate that our channel-wise attention mechanism provides a significant 3–5% improvement in accuracy over the baseline and state-of-the-art fire detection models; (ii) evaluate the impact of augmentation on fire detection, demonstrating improved performance across varied environmental conditions; (iii) comprehensive evaluation across color spaces including RGB, Grayscale, HSV, and YCbCr to analyze detection reliability; and (iv) assessment of model vulnerabilities where Fast Gradient Sign Method (FGSM) perturbations significantly impact performance, reducing accuracy to 41%. Using Local Interpretable Model-Agnostic Explanations (LIME) visualization techniques, we provide insights into model decision-making processes across both standard and adversarial conditions, highlighting important considerations for fire detection applications. Full article
(This article belongs to the Special Issue Object Detection and Recognition Based on Deep Learning)
►▼ Show Figures

Figure 1

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
Cited by 3 | Viewed by 1467
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 451 KiB  
Review
A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection
by Maria Trigka and Elias Dritsas
Sensors 2025, 25(1), 214; https://doi.org/10.3390/s25010214 - 2 Jan 2025
Cited by 5 | Viewed by 3996
Abstract
Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) [...] Read more.
Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications. Full article
(This article belongs to the Special Issue Object Detection and Recognition Based on Deep Learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop