Computer Vision, Robotics and Intelligent Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 14503

Special Issue Editors

Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: virtual Reality; computer vision; robot applications; operations research
Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: AI techniques related to unmanned surface vehicles and water environment monitoring; autonomous navigation and collision avoidance; multi-objective optimization; fast path planning
Computer Science and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK
Interests: metaheuristics; parallel computing; multi-agent systems; planning and scheduling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce the launch of a new Special Issue of the international, cross-disciplinary, scholarly open access journal Applied Sciences (IF: 2.679 (2020)), titled “Computer Vision, Robotics, and Intelligent Systems”, for which we are Guest Editors.

Computer vision is an important sub-domain in artificial intelligence (AI), playing a significant role in the research and practise of intelligent systems; moreover, it provides the key perception and computational approach of robots. Intelligent systems models and software have frequently been developed with applications in the domains of physical agents, such as robots, or in areas linked to scene perception, image processing, and many other elements of computer vision.

Our Special Issue aims to focus on the subjects of computer vision, robotics, and intelligent systems, providing a comprehensive and deep understanding of how these systems are developed and run for academia, interdisciplinary researchers, and practitioners.

Given the above, this Special Issue is directed at any aspects discussing innovation possibilities in these three aspects. Topics may include but are not limited to the following:

  • Computer graphics
  • Robotic and AI applications
  • Virtual reality 
  • Image enhancement
  • Vision for robotics, robot navigation
  • Object recognition and manipulation

Prof. Dr. Yong Yue
Dr. Xiaohui Zhu
Dr. Mehmet Aydin
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. Applied Sciences 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.

Published Papers (6 papers)

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Research

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15 pages, 9744 KiB  
Article
Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
by Dexin Li and Kai Li
Appl. Sci. 2024, 14(1), 38; https://doi.org/10.3390/app14010038 - 20 Dec 2023
Viewed by 435
Abstract
Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of [...] Read more.
Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of high-definition maps requires extensive preparation, and in rural areas where dual two-lane roads are common, there is little pre-mapping to provide high-definition maps. This paper proposes an end-to-end model called OG-Net (Overtaking Guide Net), which accomplishes overtaking tasks without map generation or communication with other vehicles. OG-Net initially evaluates the likelihood of a successful overtaking maneuver before executing the necessary actions. It incorporates the derived probability value with a set of simple parameters and utilizes a Gaussian differential controller to determine the subsequent vehicle movements. The Gaussian differential controller effectively adapts a fixed geometric curve to various driving scenarios. Unlike conventional autonomous driving models, this approach employs uncomplicated parameters rather than RNN-series networks to integrate contextual information for overtaking guidance. Furthermore, this research curated a new end-to-end overtaking dataset, CarlaLanePass, comprising first-view image sequences, overtaking success rates, and real-time vehicle status during the overtaking process. Extensive experiments conducted on diverse road scenes using the Carla platform support the validity of our model in achieving successful overtaking maneuvers. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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28 pages, 4651 KiB  
Article
Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality Analysis
by Xianbin Hong, Sheng-Uei Guan, Nian Xue, Zhen Li, Ka Lok Man, Prudence W. H. Wong and Dawei Liu
Appl. Sci. 2023, 13(3), 1241; https://doi.org/10.3390/app13031241 - 17 Jan 2023
Cited by 1 | Viewed by 1279
Abstract
Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but [...] Read more.
Artificial intelligence (AI) systems are becoming wiser, even surpassing human performances in some fields, such as image classification, chess, and Go. However, most high-performance AI systems, such as deep learning models, are black boxes (i.e., only system inputs and outputs are visible, but the internal mechanisms are unknown) and, thus, are notably challenging to understand. Thereby a system with better explainability is needed to help humans understand AI. This paper proposes a dual-track AI approach that uses reinforcement learning to supplement fine-grained deep learning-based sentiment classification. Through lifelong machine learning, the dual-track approach can gradually become wiser and realize high performance (while keeping outstanding explainability). The extensive experimental results show that the proposed dual-track approach can provide reasonable fine-grained sentiment analyses to product reviews and remarkably achieve a 133% promotion of the Macro-F1 score on the Twitter sentiment classification task and a 27.12% promotion of the Macro-F1 score on an Amazon iPhone 11 sentiment classification task, respectively. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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17 pages, 2965 KiB  
Article
Mobile Robot Tracking with Deep Learning Models under the Specific Environments
by Tongpo Zhang, Yunze Song, Zejian Kong, Tiantian Guo, Miguel Lopez-Benitez, Enggee Lim, Fei Ma and Limin Yu
Appl. Sci. 2023, 13(1), 273; https://doi.org/10.3390/app13010273 - 26 Dec 2022
Cited by 1 | Viewed by 2037
Abstract
Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution [...] Read more.
Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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25 pages, 10758 KiB  
Article
A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
by Jie Zhang, Jin Lei, Xinyan Qin, Bo Li, Zhaojun Li, Huidong Li, Yujie Zeng and Jie Song
Appl. Sci. 2022, 12(21), 11122; https://doi.org/10.3390/app122111122 - 02 Nov 2022
Cited by 7 | Viewed by 1320
Abstract
To address power transmission lines (PTLs) traveling through complex environments leading to misdetections and omissions in fitting recognition using cameras, we propose a fitting recognition approach combining depth-attention YOLOv5 and prior synthetic dataset to improve the validity of fitting recognition. First, datasets with [...] Read more.
To address power transmission lines (PTLs) traveling through complex environments leading to misdetections and omissions in fitting recognition using cameras, we propose a fitting recognition approach combining depth-attention YOLOv5 and prior synthetic dataset to improve the validity of fitting recognition. First, datasets with inspection features are automatically synthesized based on prior series data, achieving better results with a smaller data volume for the deep learning model and reducing the cost of obtaining fitting datasets. Next, a unique data collection mode is proposed using a developed flying-walking power transmission line inspection robot (FPTLIR) as the acquisition platform. The obtained image data in this collection mode has obvious time-space, stability, and depth difference, fusing the two data types in the deep learning model to improve the accuracy. Finally, a depth-attention mechanism is proposed to change the attention on the images with depth information, reducing the probability of model misdetection and omission. Test field experiments results show that compared with YOLOv5, the mAP5095 (mean average precision on step size 0.05 for thresholds from 0.5 to 0.95) of our depth-attention YOLOv5 model for fitting is 68.1%, the recall is 98.3%, and the precision is 98.3%. Among them, AP, recall, and precision increased by 5.2%, 4.8%, and 4.1%, respectively. Test field experiments verify the feasibility of the depth-attention YOLOv5. Line field experiments results show that the mAP5095 of our depth-attention YOLOv5 model for fittings is 64.6%, and the mAPs of each class are improved compared with other attention mechanisms. The inference speed of depth-attention YOLOv5 is 3 ms slower than the standard YOLOv5 model and 10 ms to 15 ms faster than other attention mechanisms, verifying the validity of the depth-attention YOLOv5. The proposed approach improves the accuracy of the fitting recognition on PTLs, providing a recognition and localization basis for the automation and intelligence of inspection robots. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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Review

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17 pages, 2842 KiB  
Review
A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport
by Hoon Lee, Indranath Chatterjee and Gyusung Cho
Appl. Sci. 2023, 13(18), 10254; https://doi.org/10.3390/app131810254 - 13 Sep 2023
Cited by 2 | Viewed by 2613
Abstract
Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While [...] Read more.
Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While smart traffic monitoring and advanced parking distribution systems have eased urban pressures, optimizing mobility remains pivotal in the context of burgeoning smart cities. However, the seaport industry grapples with formidable issues in the efficient transport of containers. In light of this, the fusion of AI and CV technology holds promise as a solution. This study pioneers a systematic review, representing a novel exploration, delving into a comprehensive evaluation of the existing literature. It scrutinizes the profound advantages AI and CV offer in constructing sustainable, efficient parking ecosystems within seaports. Our methodological approach encompasses data collection, rigorous quality assessment, and meticulous exploration of the application of CV and AI in the realm of smart parking management. The findings underscore the pivotal role of AI and CV technologies in the development of efficient, sustainable transportation systems, particularly for optimizing container movement within seaports. This research presents a comprehensive analysis of the literature in the area of the application of AI and CV technologies in optimizing parking management at seaports, shedding light on the potential for sustainable transportation solutions in this critical domain. As these technologies usher in enhancements in traffic management, parking space allocation, and container logistics within seaports, this study represents a vital and timely contribution to the field, serving as a cornerstone for future innovations in seaport management and the broader context of smart cities. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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24 pages, 8773 KiB  
Review
A Survey of Deep Learning for Electronic Health Records
by Jiabao Xu, Xuefeng Xi, Jie Chen, Victor S. Sheng, Jieming Ma and Zhiming Cui
Appl. Sci. 2022, 12(22), 11709; https://doi.org/10.3390/app122211709 - 17 Nov 2022
Cited by 7 | Viewed by 4518
Abstract
Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract [...] Read more.
Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research. Full article
(This article belongs to the Special Issue Computer Vision, Robotics and Intelligent Systems)
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