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Sensors
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Event-Driven Vision Sensor Architectures and Application Scenarios
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Event-Driven Vision Sensor Architectures and Application Scenarios

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1101

Special Issue Editor

Dr. Juan Antonio Lenero-Bardallo

E-Mail Website
Guest Editor
Institute of Microelectronics of Seville (IMSE-CNM), CSIC, Universidad de Sevilla, 41092 Sevilla, Spain
Interests: event-driven vision sensors; sun sensors; imagers; vision sensors; HDR image sensors

Special Issue Information

Dear Colleagues,

Event-based vision sensors have evolved over the last decade from proof-of-concept lab prototypes to industrial products. Many companies, such as Prophesee, Sony, and Samsung, now include them in their product catalogs. The proliferation of artificial intelligence (AI) algorithms and systems has significantly contributed to the integration of event-driven sensors as front-end devices for acquiring pre-processed information at the edge of visual scenes.

The scope of this Special Issue is threefold:

  1. Artificial intelligence algorithms and systems utilizing event-driven vision sensors or event-based data.
  2. The development and implementation of novel sensor architectures at both the pixel and system levels. We welcome competitive, modern designs that leverage the advantages of vertical integration technologies and hybridization processes.
  3. Case studies demonstrating successful applications of event-driven sensors. Examples include industrial automation, robotics, surveillance, specialized navigation, scientific instrumentation, biomedical applications, and more.

Dr. Juan Antonio Lenero-Bardallo
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.

Keywords

  • event driven
  • vision sensors
  • applications
  • event cameras applications

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Published Papers (1 paper)

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Research

19 pages, 3759 KiB  
Article
Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation
by Huimei Duan, Chenggang Guo and Yuan Ou
Sensors 2024, 24(23), 7752; https://doi.org/10.3390/s24237752 - 4 Dec 2024
Viewed by 778
Abstract
Monocular depth estimation is a central problem in computer vision and robot vision, aiming at obtaining the depth information of a scene from a single image. In some extreme environments such as dynamics or drastic lighting changes, monocular depth estimation methods based on [...] Read more.
Monocular depth estimation is a central problem in computer vision and robot vision, aiming at obtaining the depth information of a scene from a single image. In some extreme environments such as dynamics or drastic lighting changes, monocular depth estimation methods based on conventional cameras often perform poorly. Event cameras are able to capture brightness changes asynchronously but are not able to acquire color and absolute brightness information. Thus, it is an ideal choice to make full use of the complementary advantages of event cameras and conventional cameras. However, how to effectively fuse event data and frames to improve the accuracy and robustness of monocular depth estimation remains an urgent problem. To overcome these challenges, a novel Coordinate Attention Gated Recurrent Unit (CAGRU) is proposed in this paper. Unlike the conventional ConvGRUs, our CAGRU abandons the conventional practice of using convolutional layers for all the gates and innovatively designs the coordinate attention as an attention gate and combines it with the convolutional gate. Coordinate attention explicitly models inter-channel dependencies and coordinate information in space. The coordinate attention gate in conjunction with the convolutional gate enable the network to model feature information spatially, temporally, and internally across channels. Based on this, the CAGRU can enhance the information density of the sparse events in the spatial domain in the recursive process of temporal information, thereby achieving more effective feature screening and fusion. It can effectively integrate feature information from event cameras and standard cameras, further improving the accuracy and robustness of monocular depth estimation. The experimental results show that the method proposed in this paper achieves significant performance improvements on different public datasets. Full article
(This article belongs to the Special Issue Event-Driven Vision Sensor Architectures and Application Scenarios)
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