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Applied Sciences
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Robot Vision: Theory, Methods and Applications
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Robot Vision: Theory, Methods and Applications

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

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 11041

Special Issue Editors

Prof. Dr. Ke Gu

grade E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: feature extraction; regression analysis; image enhancement; image colour analysis; video signal processing; image restoration; natural scenes; sonar imaging; visual perception; edge detection; support vector machines; underwater vehicles; virtual reality
Dr. Yutao Liu

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Shandong 266100, China
Interests: feature extraction; image representation; natural scenes; neurophysiology; regression analysis; statistical analysis; visual databases; visual perception; Gaussian distribution; distortion; image classification; neural nets; optical distortion; support vector machines; unsupervised learning; image restoration; brightness; filtering theory; image sensors; image texture; interpolation; mobile computing; motion estimation

Special Issue Information

Dear Colleagues,

This Special Issue of aims to publish a collection of research contributions illustrating the recent achievements in all aspects of the development, study, and understanding of robot vision. We hope to establish a collection of papers that will be of interest to scholars in the field. Contributions in the form of full papers, reviews, and communications about related topics are very welcome.

Prof. Dr. Ke Gu
Dr. Yutao Liu
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 (3 papers)

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Research

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19 pages, 13180 KiB  
Article
Transformer High-Voltage Primary Coil Quality Detection Method Based on Machine Vision
by Kewei Sun, Jiazhong Xu, Shiyi Zheng and Ningshuo Zhang
Appl. Sci. 2023, 13(3), 1480; https://doi.org/10.3390/app13031480 - 22 Jan 2023
Cited by 1 | Viewed by 1499
Abstract
Aiming at the problems of low efficiency and low accuracy in manual detection of winding angle and wire spacing during automatic winding of high-voltage primary coils of transmission and distribution transformers, a detection scheme using machine vision is proposed. Firstly, the coil image [...] Read more.
Aiming at the problems of low efficiency and low accuracy in manual detection of winding angle and wire spacing during automatic winding of high-voltage primary coils of transmission and distribution transformers, a detection scheme using machine vision is proposed. Firstly, the coil image is acquired by the industrial camera, the detection region is segmented, and the ROI (region of interest) image is pre-processed. For winding angle detection, we propose a slicing method for image graying to reduce the interference caused by uneven light irradiation. The gray image is converted to a binary image, and wire skeleton extraction is performed; the skeleton is identified using the Hough transform for feature straight lines, and the winding angle is then calculated. For wire spacing detection, we propose an intersection of the perpendicular lines method, which extracts edge coordinates using contour images and performs endpoint pixel expansion and shape classification. Use the intersection of the vertical lines to determine the centroid coordinates of the wire outline, calculate the pixel distance of the adjacent centroid, and obtain the wire spacing by combining pixel calibration. Comparison experiments have shown that the solution has a high detection accuracy (0.01 mm), and the error of the integrated detection results is not higher than 10%, which enables the real-time detection of coil winding status and corrects the winding process according to the visual real-time detection result to improve the finished product quality of coils. Full article
(This article belongs to the Special Issue Robot Vision: Theory, Methods and Applications)
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17 pages, 7886 KiB  
Article
AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
by Yanhua Shao, Xingping Zhang, Hongyu Chu, Xiaoqiang Zhang, Duo Zhang and Yunbo Rao
Appl. Sci. 2022, 12(7), 3627; https://doi.org/10.3390/app12073627 - 03 Apr 2022
Cited by 20 | Viewed by 2775
Abstract
Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method [...] Read more.
Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS. Full article
(This article belongs to the Special Issue Robot Vision: Theory, Methods and Applications)
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Review

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26 pages, 56447 KiB  
Review
Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
by Chang Liu, Jingxin Lu, Hongbo Yang and Kai Guo
Appl. Sci. 2022, 12(9), 4540; https://doi.org/10.3390/app12094540 - 29 Apr 2022
Cited by 12 | Viewed by 5687
Abstract
Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders [...] Read more.
Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders consistently report that the needs of some users are not being met. The purpose of this review is to understand the reasons why these user needs are not being adequately addressed, to explore research on hand rehabilitation robots, to review their current state of research in recent years, and to summarize future trends in the hope that it will be useful to researchers in this research area. This review summarizes the techniques in this paper in a systematic way. We first provide a comprehensive review of research institutions, commercial products, and literature. Thus, the state of the art and deficiencies of functional hand rehabilitation robots are sought and guide the development of subsequent hand rehabilitation robots. This review focuses specifically on the actuation and control of hand functional rehabilitation robots, as user needs are primarily focused on actuation and control strategies. We also review hand detection technologies and compare them with patient needs. The results show that the trends in recent years are more inclined to pursue new lightweight materials to improve hand adaptability, investigating intelligent control methods for human-robot interaction in hand functional rehabilitation robots to improve control robustness and accuracy, and VR virtual task positioning to improve the effectiveness of active rehabilitation training. Full article
(This article belongs to the Special Issue Robot Vision: Theory, Methods and Applications)
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