Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Vision Sensors: Image Processing Technologies and Applications

Special Issue Editors
Special Issue Information
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 14722

Share This Special Issue

Special Issue Editor

Prof. Dr. Leandro A. F. Fernandes

E-Mail Website
Guest Editor

Instituto de Computação, Universidade Federal Fluminense (UFF), Avenida General Milton Tavares de Souza, s/n, Room 534 São Domingos, Niterói 24210-346, RJ, Brazil
Interests: shape and pattern detection; image features; image metrology; image-based tracking; 3D scanning; surface reconstruction; deep learning; data embedding

Special Issue Information

Dear Colleagues,

Computer vision and optical technologies have become attractive alternatives for metrology, chemical analysis, and non-destructive structural health monitoring and evaluation of aerospace, civil, and mechanical engineering structures. The popularization of vision techniques for creating sensors for a wide range of purposes has been driven mainly by advances in digital cameras, image processing algorithms, and deep learning approaches that have leveraged the development of new reliable tools.

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 vision sensors.

Potential topics include but are not limited to:

Dynamic vision sensors (event cameras);
Image-based chemical analysis;
Image-based aerospace, civil, and mechanical inspection;
Image-based metrology;
Image-based non-destructive structural health monitoring and evaluation;
Vision sensor applications;
Vision sensor tools;
Datasets for vision sensor evaluation;
State-of-the-art review of vision sensors.

Prof. Dr. Leandro A. F. Fernandes
Guest Editor

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.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research, Review

2 pages, 143 KiB

Open AccessEditorial

Editorial to the Special Issue “Vision Sensors: Image Processing Technologies and Applications”

by Leandro A. F. Fernandes

Sensors 2024, 24(6), 1803; https://doi.org/10.3390/s24061803 - 11 Mar 2024

Viewed by 512

Abstract

Computer vision and optical technologies have become attractive alternatives for metrology, chemical analysis, and non-destructive structural health monitoring and the evaluation of aerospace, civil, and mechanical engineering structures [...] Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

Research

Jump to: Editorial, Review

13 pages, 839 KiB

Open AccessArticle

Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition

by Wenyuan Sun, Wentang Chen, Runxiang Huang and Jing Tian

Sensors 2024, 24(3), 855; https://doi.org/10.3390/s24030855 - 28 Jan 2024

Viewed by 743

Abstract

The goal of visual place recognition (VPR) is to determine the location of a query image by identifying its place in a collection of image databases. Visual sensor technologies are crucial for visual place recognition as they allow for precise identification and location of query images within a database. Global descriptor-based VPR methods face the challenge of accurately capturing the local specific regions within a scene; consequently, it leads to an increasing probability of confusion during localization in such scenarios. To tackle feature extraction and feature matching challenges in VPR, we propose a modified patch-NetVLAD strategy that includes two new modules: a context-aware patch descriptor and a context-aware patch matching mechanism. Firstly, we propose a context-driven patch feature descriptor to overcome the limitations of global and local descriptors in visual place recognition. This descriptor aggregates features from each patch’s surrounding neighborhood. Secondly, we introduce a context-driven feature matching mechanism that utilizes cluster and saliency context-driven weighting rules to assign higher weights to patches that are less similar to densely populated or locally similar regions for improved localization performance. We further incorporate both of these modules into the patch-NetVLAD framework, resulting in a new approach called contextual patch-NetVLAD. Experimental results are provided to show that our proposed approach outperforms other state-of-the-art methods to achieve a Recall@10 score of

99.82

P i t t s b u r g h 30 k

99.82

on FMDataset, and

97.68

on our benchmark dataset. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Figure 1

38 pages, 112154 KiB

Open AccessArticle

Deep Learning Tone-Mapping and Demosaicing for Automotive Vision Systems

by Ana Stojkovic, Jan Aelterman, David Van Hamme, Ivana Shopovska and Wilfried Philips

Sensors 2023, 23(20), 8507; https://doi.org/10.3390/s23208507 - 17 Oct 2023

Viewed by 1503

Abstract

High dynamic range (HDR) imaging technology is increasingly being used in automated driving systems (ADS) for improving the safety of traffic participants in scenes with strong differences in illumination. Therefore, a combination of HDR video, that is video with details in all illumination regimes, and (HDR) object perception techniques that can deal with this variety in illumination is highly desirable. Although progress has been made in both HDR imaging solutions and object detection algorithms in the recent years, they have progressed independently of each other. This has led to a situation in which object detection algorithms are typically designed and constantly improved to operate on 8 bit per channel content. This makes these algorithms not ideally suited for use in HDR data processing, which natively encodes to a higher bit-depth (12 bits/16 bits per channel). In this paper, we present and evaluate two novel convolutional neural network (CNN) architectures that intelligently convert high bit depth HDR images into 8-bit images. We attempt to optimize reconstruction quality by focusing on ADS object detection quality. The first research novelty is to jointly perform tone-mapping with demosaicing by additionally successfully suppressing noise and demosaicing artifacts. The first CNN performs tone-mapping with noise suppression on a full-color HDR input, while the second performs joint demosaicing and tone-mapping with noise suppression on a raw HDR input. The focus is to increase the detectability of traffic-related objects in the reconstructed 8-bit content, while ensuring that the realism of the standard dynamic range (SDR) content in diverse conditions is preserved. The second research novelty is that for the first time, to the best of our knowledge, a thorough comparative analysis against the state-of-the-art tone-mapping and demosaicing methods is performed with respect to ADS object detection accuracy on traffic-related content that abounds with diverse challenging (i.e., boundary cases) scenes. The evaluation results show that the two proposed networks have better performance in object detection accuracy and image quality, than both SDR content and content obtained with the state-of-the-art tone-mapping and demosaicing algorithms. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Figure 1

15 pages, 4129 KiB

Open AccessArticle

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning

by Fengxia Xu, Zhenyang Xu, Zhongda Lu, Chuanshui Peng and Shiwei Yan

Sensors 2023, 23(18), 7716; https://doi.org/10.3390/s23187716 - 7 Sep 2023

Viewed by 765

Abstract

The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset’s character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset’s characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Figure 1

17 pages, 3981 KiB

Open AccessArticle

Real-Time Segmentation of Unstructured Environments by Combining Domain Generalization and Attention Mechanisms

by Nuanchen Lin, Wenfeng Zhao, Shenghao Liang and Minyue Zhong

Sensors 2023, 23(13), 6008; https://doi.org/10.3390/s23136008 - 28 Jun 2023

Cited by 3 | Viewed by 1357

Abstract

This paper presents a focused investigation into real-time segmentation in unstructured environments, a crucial aspect for enabling autonomous navigation in off-road robots. To address this challenge, an improved variant of the DDRNet23-slim model is proposed, which includes a lightweight network architecture and reclassifies ten different categories, including drivable roads, trees, high vegetation, obstacles, and buildings, based on the RUGD dataset. The model’s design includes the integration of the semantic-aware normalization and semantic-aware whitening (SAN–SAW) module into the main network to improve generalization ability beyond the visible domain. The model’s segmentation accuracy is improved through the fusion of channel attention and spatial attention mechanisms in the low-resolution branch to enhance its ability to capture fine details in complex scenes. Additionally, to tackle the issue of category imbalance in unstructured scene datasets, a rare class sampling strategy (RCS) is employed to mitigate the negative impact of low segmentation accuracy for rare classes on the overall performance of the model. Experimental results demonstrate that the improved model achieves a significant 14% increase mIoU in the invisible domain, indicating its strong generalization ability. With a parameter count of only 5.79M, the model achieves mAcc of 85.21% and mIoU of 77.75%. The model has been successfully deployed on a a Jetson Xavier NX ROS robot and tested in both real and simulated orchard environments. Speed optimization using TensorRT increased the segmentation speed to 30.17 FPS. The proposed model strikes a desirable balance between inference speed and accuracy and has good domain migration ability, making it applicable in various domains such as forestry rescue and intelligent agricultural orchard harvesting. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Figure 1

15 pages, 3024 KiB

Open AccessArticle

End-to-End Implementation of a Convolutional Neural Network on a 3D-Integrated Image Sensor with Macropixel Array

by Maria Lepecq, Thomas Dalgaty, William Fabre and Stéphane Chevobbe

Sensors 2023, 23(4), 1909; https://doi.org/10.3390/s23041909 - 8 Feb 2023

Cited by 3 | Viewed by 1666

Abstract

Three-dimensional-integrated focal-plane array image processor chips offer new opportunities to implement highly parallelised computer vision algorithms directly inside sensors. Neural networks in particular can perform highly complex machine vision tasks, and therefore their efficient implementation in such imagers are of significant interest. However, studies with existing pixel-processor array chips have focused on the implementation of a subset of neural network components—notably convolutional kernels—on pixel processor arrays. In this work, we implement a continuous end-to-end pipeline for a convolutional neural network from the digitisation of incoming photons to the output prediction vector on a macropixel processor array chip (where a single processor acts on group of pixels). Our implementation performs inference at a rate between 265 and 309 frames per second, directly inside of the sensor, by exploiting the different levels of parallelism available. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

32 pages, 1693 KiB

Open AccessReview

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review

by Andrada-Livia Cîrneanu, Dan Popescu and Dragoș Iordache

Sensors 2023, 23(16), 7092; https://doi.org/10.3390/s23167092 - 10 Aug 2023

Cited by 9 | Viewed by 7325

Abstract

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper’s scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified. Full article

(This article belongs to the Special Issue Vision Sensors: Image Processing Technologies and Applications)

► Show Figures

Journal Menu

Journal Browser

Vision Sensors: Image Processing Technologies and Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (7 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI