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AI-Driven Sensing for Small Object Recognition

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 6361

Special Issue Editors


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Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: embedded systems; micro-processor and robot; stochastic signal analysis; multi-media technology and application; cloud computing; compile principle

E-Mail Website
Guest Editor
1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
2. Department of Electrical & Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: resource allocation; learning (artificial intelligence)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: trusted machine learning algorithms

Special Issue Information

Dear Colleagues,

Small-object recognition is a fundamental research problem in the field of computer vision and sensing. It is difficult to detect small-objects due to many practical factors, such as indistinguishable features, low resolution, complicated backgrounds, and limited contextual information. The success of small-object recognition could enable many important tasks, such as object tracking, instance segmentation, image captioning, action recognition, and scene understanding. Small-object recognition also plays an important role in real-world scenarios, e.g., urban planning, robotic vision, autonomous driving, intelligent transportation, intelligent healthcare, military reconnaissance and surveillance, where objects of interest could be of a very small size. The integration of advanced artificial intelligence (AI) and sensing technologies could contribute potential solutions to this challenging task. As a result, this Special Issue aims to present the latest advances in deep models, algorithms, and applications that contribute to AI-driven small-object recognition. The main topics include (but are not limited to):

  • Effective feature extraction for small-object recognition;
  • Data augmentation for small-object recognition;
  • Cross-/multi-modal learning for small-object recognition;
  • Meta learning for small-object recognition;
  • Explainable models for small-object recognition;
  • Robust learning for small-object recognition;
  • Novel benchmarks for small-object recognition;
  • Novel applications for small-object recognition.

Prof. Dr. Jianqiang Li
Prof. Dr. Victor C. M. Leung
Dr. Houbing Song
Dr. Jie Chen
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.

Published Papers (1 paper)

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Research

27 pages, 12559 KiB  
Article
A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference
by Adel Ammar, Anis Koubaa, Wadii Boulila, Bilel Benjdira and Yasser Alhabashi
Sensors 2023, 23(4), 2120; https://doi.org/10.3390/s23042120 - 13 Feb 2023
Cited by 15 | Viewed by 5850
Abstract
Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, [...] Read more.
Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates’ Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models’ accuracy by taking advantage of the temporally redundant information of the video stream’s frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022. Full article
(This article belongs to the Special Issue AI-Driven Sensing for Small Object Recognition)
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