Intelligent Detection and Identification System Based on Computer Vision Technology

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

Deadline for manuscript submissions: 10 May 2024 | Viewed by 2725

Special Issue Editors

Research Center of Graphic Communication and Printing and Packaging, Wuhan University, Wuhan 430079, China
Interests: infrared remote sening image target detection; thermal infrared remote sensing; deep learning
Department of Technology, NORCE Norwegian Research Centre, 4879 Grimstad, Norway
Interests: Tsetlin machine; learning automata; mathematical analysis on learning algorithms; deep learning
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
Interests: change detection of remote sensing imagery; hyperspectral remote sensing; intelligent agricultural remote sensing
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Special Issue Information

Dear Colleagues,

Computer vision is a sector of artificial intelligence that combines the theories and techniques of machine learning, pattern recognition and deep learning to enable computers to see, recognize and analyze things in photos and videos in the same way that people do. There are increasing uses for computer vision in intelligent detection and identification system, and the applications of those techniques include medical lesion detection, food sorting, vehicle detection and tracking, remote sensing image target detection and analysis, etc.

This Special Issue is dedicated to disseminating new theories/approaches, especially those using the existing models, concepts, and architectural designs, in intelligent detection and identification based on computer vision technology.

Subjects up for discussion in this Special Issue are not limited to modern methods, technologies, and further handling of target detection and identification, but this Issue will also focus on the verification of their properties in different practical fields.

Dr. Liqin Cao
Dr. Xuan Zhang
Prof. Dr. Lifei Wei
Guest Editors

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Keywords

  • target detection and identification
  • computer vision
  • artificial intelligence, machine learning and deep learning
  • learning automata
  • detection dataset

Published Papers (3 papers)

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Research

17 pages, 5607 KiB  
Article
Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
by Lei Cai, Congling Zhou, Yongqiang Wang, Hao Wang and Boyu Liu
Appl. Sci. 2023, 13(23), 12617; https://doi.org/10.3390/app132312617 - 23 Nov 2023
Viewed by 757
Abstract
(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false [...] Read more.
(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems. Full article
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13 pages, 4935 KiB  
Article
Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector
by Zhendong Shang, Qinzhang Wei and Zhaoying Li
Appl. Sci. 2023, 13(14), 8450; https://doi.org/10.3390/app13148450 - 21 Jul 2023
Viewed by 623
Abstract
The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used [...] Read more.
The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm. Full article
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16 pages, 9288 KiB  
Article
Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures
by Xavier Arzola-Villegas, Carlos Báez, Roderic Lakes, Donald S. Stone, Jane O’Dell, Pavel Shevchenko, Xianghui Xiao, Francesco De Carlo and Joseph E. Jakes
Appl. Sci. 2023, 13(14), 8146; https://doi.org/10.3390/app13148146 - 13 Jul 2023
Cited by 2 | Viewed by 913
Abstract
To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time [...] Read more.
To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials. Full article
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