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Electronics
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New Trends in AI-Assisted Computer Vision
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New Trends in AI-Assisted Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 5902

Special Issue Editors

Dr. Huikai Shao

E-Mail Website
Guest Editor
School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: computer vision; biometrics; palmprint recognition; transfer learning
Dr. Dan Zeng

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: biometrics; visual computing; face restoration
Dr. Shuiwang Li

E-Mail Website
Guest Editor
College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
Interests: computer vision; pattern recognition; visual tracking; object detection

Special Issue Information

Dear Colleagues,

In recent years, the field of computer vision has undergone significant advancements, primarily driven by the integration of artificial intelligence (AI) techniques. AI-assisted computer vision represents the confluence of advanced machine learning algorithms, neural networks, and high-resolution imaging technologies, enabling machines to interpret and understand visual data with unprecedented accuracy and depth. These advancements are poised to unlock new possibilities across industries, fostering smarter automation, enhanced user experiences, and safer environments. As research progresses and technology matures, AI-assisted computer vision will undoubtedly open up new possibilities and solutions in previously unimaginable applications. Our Special Issue is now seeking contributions from researchers to share their original papers in this field, including pattern recognition, image processing, feature extraction, object detection, biometrics, semantic segmentation, video analysis, their applications, and so on.

Dr. Huikai Shao
Dr. Dan Zeng
Dr. Shuiwang 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

  • image processing
  • feature extraction
  • object detection
  • object tracking
  • image segmentation
  • biometrics
  • semantic segmentation
  • edge detection
  • deep learning
  • visual recognition
  • 3D vision
  • pose estimation
  • behaviour analysis
  • scene understanding
  • visual question answering
  • augmented reality
  • virtual reality

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Published Papers (3 papers)

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Research

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21 pages, 1163 KiB  
Article
Improved Maneuver Detection-Based Multiple Hypothesis Bearing-Only Target Tracking Algorithm
by Xinan Liu, Panlong Wu, Yuming Bo, Chunhao Liu, Haitao Hu and Shan He
Electronics 2025, 14(7), 1439; https://doi.org/10.3390/electronics14071439 - 2 Apr 2025
Viewed by 257
Abstract
In ground-based bearing-only tracking of multiple maneuvering targets, there are difficulties in data association due to the reliance solely on azimuth information, making it challenging to distinguish and identify multiple targets. This problem is particularly pronounced when targets are close or overlapping, leading [...] Read more.
In ground-based bearing-only tracking of multiple maneuvering targets, there are difficulties in data association due to the reliance solely on azimuth information, making it challenging to distinguish and identify multiple targets. This problem is particularly pronounced when targets are close or overlapping, leading to disassociation or target loss. Moreover, bearing-only information struggles to accurately capture the dynamic changes in maneuvering targets, significantly affecting tracking accuracy. To address these issues, this paper proposes an Improved Maneuver Detection-Based Multiple Hypothesis Bearing-Only Target Tracking (IMD-MHRPCKF) algorithm. To begin with, the observation range is segmented into multiple sub-intervals through a distance parameterization technique, and within each sub-interval, a Cubature Kalman Filter (CKF) is applied. The Multiple Hypothesis Tracking (MHT) algorithm is then used for data association, solving the measurement ambiguity problem. To detect target maneuvers, the sliding window average of the innovation sequence is calculated. When a target maneuver is detected, the sub-filter parameters are reinitialized to ensure filter stability. In contrast, if no maneuver is detected, the filter parameters remain unchanged. Finally, simulations are used to compare this algorithm with various other algorithms. The results show that the proposed algorithm significantly improves system robustness, reduces tracking errors, and effectively tracks bearing-only multiple maneuvering targets. Full article
(This article belongs to the Special Issue New Trends in AI-Assisted Computer Vision)
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19 pages, 17909 KiB  
Article
Nighttime Pothole Detection: A Benchmark
by Min Ling, Quanjun Shi, Xin Zhao, Wenzheng Chen, Wei Wei, Kai Xiao, Zeyu Yang, Hao Zhang, Shuiwang Li, Chenchen Lu and Yufan Zeng
Electronics 2024, 13(19), 3790; https://doi.org/10.3390/electronics13193790 - 24 Sep 2024
Viewed by 2004
Abstract
In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can [...] Read more.
In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety. Full article
(This article belongs to the Special Issue New Trends in AI-Assisted Computer Vision)
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Review

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20 pages, 7410 KiB  
Review
Toward Automated Fabric Defect Detection: A Survey of Recent Computer Vision Approaches
by Rui Carrilho, Ehsan Yaghoubi, José Lindo, Kailash Hambarde and Hugo Proença
Electronics 2024, 13(18), 3728; https://doi.org/10.3390/electronics13183728 - 20 Sep 2024
Cited by 6 | Viewed by 2890
Abstract
Defect detection is a crucial part of the pipeline in many industries. In the textile industry, it is especially important, as it will affect the quality and price of the final product. However, it is mostly performed by human agents, who have been [...] Read more.
Defect detection is a crucial part of the pipeline in many industries. In the textile industry, it is especially important, as it will affect the quality and price of the final product. However, it is mostly performed by human agents, who have been reported to have poor performance, along with requiring a costly and time-consuming training process. As such, methods to automate the process have been increasingly explored throughout the last 20 years. While there are many traditional approaches to this problem, with the advent of deep learning, machine learning-based approaches now constitute the majority of all possible approaches. Other articles have explored traditional approaches and machine learning approaches in a more general way, detailing their evolution over time. In this review, we summarize the most important advancements in the last 5 years and focus mostly on machine learning-based approaches. We also outline the most promising avenues of research in the future. Full article
(This article belongs to the Special Issue New Trends in AI-Assisted Computer Vision)
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