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Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and...
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Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 3451

Special Issue Editors

Dr. Shanglian Zhou

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: computer vision; deep learning; structural health monitoring; intelligent transportation systems; bayesian statistics; cyber-physical systems
Dr. Igor Lashkov

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: machine learning; computer vision; deep neural networks; object detection; object tracking; intelligent transportation systems; connected vehicle applications
Dr. Hanyi Yang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: AI in transportation; intelligent transportation systems; traffic flow theory; traffic cybersecurity; traffic safety
Dr. Runze Yuan

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing, 100084, China
Interests: traffic status perception and fusion; edge computing; intelligent vehicle-infrastructure cooperation systems (i-VICS); intelligent sensing and control; connected autonomous vehicles

Special Issue Information

Dear Colleagues,

Intelligent Transportation Systems (ITS) refer to the integration of advanced electronics and information technology during different stages of transportation. Their purpose is to improve the safety, serviceability, and reliability of the autonomous operation of transportation system components, such as vehicles and road infrastructure.

In the past decade, emerging technologies in computer vision, Artificial Intelligence (AI), and smart sensing have greatly facilitated the development of ITS in various industries, e.g., the autonomous vehicles industry. Aided by cutting-edge AI technologies, especially deep learning algorithms, the capability, sustainability, and robustness of ITS applications in handling pattern recognition tasks under real-world data complexities have been impressively improved. 

Nevertheless, upon incorporating these emerging technologies into ITS applications, a series of challenges are yet to be properly addressed, and they require further attention from researchers and professionals. Several pressing concerns are as follows: the impact of adverse weather conditions; object detection and tracking under occlusion; transferability and generalizability of deep learning models; and multi-modal data fusion, processing, and feature extraction. Therefore, research efforts need to be devoted to developing innovative strategies based on advanced AI, computer vision, and sensing techniques to tackle these practical challenges and, thus, facilitate the design, analysis, and optimization of future ITS applications. 

This Special Issue welcomes original research articles and literature reviews on innovative developments and applications in the field of deep learning-aided computer vision and pattern recognition in ITS. Submissions are particularly welcome in (but not limited to) the research areas specified by the keywords below.

We look forward to receiving your contributions.

Dr. Shanglian Zhou
Dr. Igor Lashkov
Dr. Hanyi Yang
Dr. Runze Yuan
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

  • computer vision
  • deep learning applications
  • pattern recognition
  • intelligent transportation systems
  • intelligent vehicle–infrastructure cooperation systems (i-VICS)
  • civil and transportation infrastructure
  • connected autonomous vehicles
  • autonomous driving
  • unmanned aerial systems
  • vehicle-to-everything (V2X) communication
  • object detection
  • object tracking
  • data association
  • LiDAR technology
  • sensor fusion
  • adverse weather conditions
  • traffic big data analytics
  • traffic safety and risk assessment
  • edge computing
  • bayesian statistics
  • smart traffic management

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Published Papers (3 papers)

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Research

20 pages, 3267 KiB  
Article
Enhanced Receptive Field and Multi-Branch Feature Extraction in YOLO for Bridge Surface Defect Detection
by Wenyuan Zhu, Tao Yang and Ruexue Zhang
Electronics 2025, 14(5), 989; https://doi.org/10.3390/electronics14050989 - 28 Feb 2025
Viewed by 652
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly utilized for bridge inspections and play a crucial role in detecting defects. Nevertheless, accurately identifying defects at various scales in complex contexts remains a significant challenge. To address this issue, we propose RDS-YOLO, an advanced algorithm based [...] Read more.
Unmanned Aerial Vehicles (UAVs) are increasingly utilized for bridge inspections and play a crucial role in detecting defects. Nevertheless, accurately identifying defects at various scales in complex contexts remains a significant challenge. To address this issue, we propose RDS-YOLO, an advanced algorithm based on YOLOv8n, designed to enhance small-scale defect detection through the integration of shallow, high-resolution features. The introduction of the RFW (Receptive Field Weighting) module dynamically expands the receptive field and balances multi-scale detection accuracy. Additionally, the DSF-Bottneck (Dilated Separable Fusion) module further optimizes feature extraction, emphasizing the representation of small defects against complex backgrounds. The SA-Head (Shuffle Attentio) module, with shared parameters, precisely localizes defect zones while reducing computational costs. Furthermore, the EigenCAM technique improves the interpretability of the model’s output, offering valuable insights for maintenance and monitoring tasks. The experimental results demonstrate that RDS-YOLO outperforms YOLOv8n, achieving a 3.7% increase in average detection precision and a 6.7% improvement in small defect detection accuracy. Full article
(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)
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15 pages, 3906 KiB  
Article
Wearable AR System for Real-Time Pedestrian Conflict Alerts Using Live Roadside Data
by Adrian Lin, Hao Xu and Zhihui Chen
Electronics 2025, 14(1), 99; https://doi.org/10.3390/electronics14010099 - 29 Dec 2024
Cited by 1 | Viewed by 1250
Abstract
Pedestrian safety is increasingly becoming a major concern within and around intersections. This paper outlines a novel approach to enhancing pedestrian safety using a wearable augmented reality (AR) system integrated with live roadside light detection and range (LiDAR) sensor data. The proposed system [...] Read more.
Pedestrian safety is increasingly becoming a major concern within and around intersections. This paper outlines a novel approach to enhancing pedestrian safety using a wearable augmented reality (AR) system integrated with live roadside light detection and range (LiDAR) sensor data. The proposed system aims to provide real-time and spatially located warnings to pedestrians, thereby helping them proactively evade potential accidents. The system architecture is built to lessen the burden on edge devices, such as the AR headset itself, and ensure that most of the processor-heavy computations are performed within the server. The effectiveness of this system has been demonstrated and evaluated through various tests, including latency measurements. Point cloud accuracy measurements were recorded with an average offset of approximately 0.73 inches between a conflicting vehicle’s actual location and the visualized object location through AR. This paper also discusses the potential of this system with respect to vehicle-to-vehicle (V2V) systems and other societal benefits. Full article
(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)
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16 pages, 3278 KiB  
Article
Real-Time Wild Horse Crossing Event Detection Using Roadside LiDAR
by Ziru Wang, Hao Xu, Fei Guan and Zhihui Chen
Electronics 2024, 13(19), 3796; https://doi.org/10.3390/electronics13193796 - 25 Sep 2024
Cited by 1 | Viewed by 901
Abstract
Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging [...] Read more.
Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article
(This article belongs to the Special Issue Advances, Applications, and Challenges of Deep Learning-Aided Computer Vision and Pattern Recognition in Intelligent Transportation)
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