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Electronics
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Recent Advances in Image and Video Processing Using Artificial Intelligence
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Recent Advances in Image and Video Processing Using Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 916

Special Issue Editors

Dr. Meng Li

E-Mail Website
Guest Editor
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
Interests: deep learning; machine learning; neuroscience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun-Bao Li

E-Mail Website
Guest Editor
School of Computing, Harbin Institute of Technology, Harbin 150001, China
Interests: machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, artificial intelligence (AI) is a major topic that is attracting a lot of attention in many scientific fields. Artificial intelligence aims to understand the essence of biological intelligence and develop intelligent systems that can mimic the performance of human intelligence. Over the past decade, breakthroughs regarding AI have provided unprecedented tools for analyzing massive amounts of data, such as the rapidly growing amount of images and videos produced every day. For this Special Issue, we encourage researchers to provide contributions in this historical era of AI, as this Special Issue aims to synthesize the current state of knowledge on AI and define the most exciting approaches and techniques that could potentially be used to advance image and video processing using AI.

 Potential research topics:

  • Object detection and tracking based on AI;
  • Generative adversarial network (GAN)-based image and video processing and recognition;
  • AI and blockchain applications in image and video processing;
  • AI-based image and video processing in brain research and neurological disease diagnostics;
  • AI for brain–computer interfaces;
  • Image interpretation based on AI.

Dr. Meng Li
Prof. Dr. Jun-Bao Li
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. Electronics 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 2400 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
  • tracking
  • image processing
  • video processing
  • artificial intelligence (AI)

Published Papers (2 papers)

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Research

14 pages, 1822 KiB  
Article
Efficient and Lightweight Neural Network for Hard Hat Detection
by Chenxi He, Shengbo Tan, Jing Zhao, Daji Ergu, Fangyao Liu, Bo Ma and Jianjun Li
Electronics 2024, 13(13), 2507; https://doi.org/10.3390/electronics13132507 - 26 Jun 2024
Viewed by 239
Abstract
Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are [...] Read more.
Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model’s detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection. Full article
(This article belongs to the Special Issue Recent Advances in Image and Video Processing Using Artificial Intelligence)
15 pages, 6102 KiB  
Article
Active Visual Perception Enhancement Method Based on Deep Reinforcement Learning
by Zhonglin Yang, Hao Fang, Huanyu Liu, Junbao Li, Yutong Jiang and Mengqi Zhu
Electronics 2024, 13(9), 1654; https://doi.org/10.3390/electronics13091654 - 25 Apr 2024
Viewed by 438
Abstract
Traditional object detection methods using static cameras are constrained by their limited perspectives, hampering the effective detection of low-confidence targets. To address this challenge, this study introduces a deep reinforcement learning-based visual perception enhancement technique. This approach leverages pan–tilt–zoom (PTZ) cameras to achieve [...] Read more.
Traditional object detection methods using static cameras are constrained by their limited perspectives, hampering the effective detection of low-confidence targets. To address this challenge, this study introduces a deep reinforcement learning-based visual perception enhancement technique. This approach leverages pan–tilt–zoom (PTZ) cameras to achieve active vision, enabling them to autonomously make decisions and actions tailored to the current scene and object detection outcomes. This optimization enhances both the object detection process and information acquisition, significantly boosting the intelligent perception capabilities of PTZ cameras. Experimental findings demonstrate the robust generalization capabilities of this method across various object detection algorithms, resulting in an average confidence level improvement of 23.80%. Full article
(This article belongs to the Special Issue Recent Advances in Image and Video Processing Using Artificial Intelligence)
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