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Applied Sciences
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Deep Learning for Image Recognition and Processing
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Deep Learning for Image Recognition and Processing

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 6652

Special Issue Editors

Dr. Jiangyun Li

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: image and video semantic segmentation; deep learning; industrial process control; industrial intelligence; natural language processing; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Dr. Tianxiang Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: machine learning; deep learning; remote sensing image analysis; image segmentation
Dr. Peixian Zhuang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: deep learning; machine learning; image processing

Special Issue Information

Dear Colleagues,

Deep learning technology has been drawing increasing interest for a wide range of computer vision and image analysis tasks, such as image classification, image segmentation, object detection and so on. A number of applications can be utilized by deep learning technology, such as industrial intelligence, remote sensing image analysis and autonomous driving. However, deep learning technology has faced some challenging problems in various applications, limiting image recognition and processing performance. Some modified deep learning on model-based or module-based strategies cannot cope with the volume of problems, and this must be urgently addressed: for example, how to improve deep learning model accuracy by using a limited sample size, or how to propose an explainable deep learning model to make it precise and reliable. As a result, this Special Issue aims to cover novel strategies on deep learning algorithms for image recognition and processing by using reliable, optimized and hybrid deep learning algorithms in a number of applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Deep learning image analysis in civil applications (e.g., industrial intelligence, remote sensing, biology and medical image).
  • Novel training strategies when facing small sample size problems.
  • Explainable deep learning models to innovate deep learning model construction and improve model reliability.
  • Image classification, segmentation and object targeting using deep learning algorithms.
  • The transfer of learning and knowledge distillation for image processing.

We look forward to receiving your contributions.

Prof. Dr. Jiangyun Li
Dr. Tianxiang Zhang
Dr. Peixian Zhuang
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.

Keywords

  • machine learning/deep learning
  • image segmentation
  • image classification
  • object detection
  • remote sensing image analysis

Published Papers (5 papers)

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Research

18 pages, 5456 KiB  
Article
Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
by Ganglong Duan, Shaoyang Zhang, Yanying Shang and Weiwei Kong
Appl. Sci. 2024, 14(8), 3451; https://doi.org/10.3390/app14083451 - 19 Apr 2024
Viewed by 201
Abstract
Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, [...] Read more.
Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen’s kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy. Full article
(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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19 pages, 3357 KiB  
Article
Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor
by Sandisiwe Nomqupu, Athule Sali, Adolph Nyamugama and Naledzani Ndou
Appl. Sci. 2024, 14(7), 2670; https://doi.org/10.3390/app14072670 - 22 Mar 2024
Viewed by 540
Abstract
This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted [...] Read more.
This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Entropy thresholding segmentation was employed to classify potholes. The sigmoid calibration function was used to reconfigure spectral radiance properties of the UAV spectral bands to pothole features. The descriptive statistics was computed to determine radiance threshold values to be used in demarcating potholes from the reconfigured or calibrated spectral bands. The performance of the sigmoid calibration function was evaluated by analyzing the area under curve (AUC) results generated using the Relative Operating Characteristic (ROC) technique. Spectral radiance pattern analysis of the pothole surface revealed high radiance values in the red channel and low radiance values in the near-infrared (NIR) channels of the spectrum. The sigmoid calibration function radiometrically reconfigured UAV spectral bands based on a total of 500 sampled pixels of pothole surface obtained from all the spectral channels. Upon successful calibration of UAV radiometric properties to pothole surface, the reconfigured mean radiance values for pothole surface were noted to be 0.868, 0.886, 0.944, 0.211 and 0.863 for blue, green, red, NIR and red edge, respectively. The area under curve (AUC) results revealed the r2 values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. Overestimation of pothole 1 by both original and calibrated spectral channels was noted and can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. The results of this study emphasize the significance of reconfiguring radiometric properties of the UAV imagery for improved recognition of potholes. Full article
(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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15 pages, 4292 KiB  
Article
LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction
by Xinyu Hou, Peng Chen and Haishuo Gu
Appl. Sci. 2024, 14(4), 1558; https://doi.org/10.3390/app14041558 - 15 Feb 2024
Viewed by 700
Abstract
Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, [...] Read more.
Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+. Full article
(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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20 pages, 2965 KiB  
Article
A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks
by Li Fan and Jiancheng Zou
Appl. Sci. 2023, 13(22), 12299; https://doi.org/10.3390/app132212299 - 14 Nov 2023
Viewed by 950
Abstract
Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied [...] Read more.
Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement. Full article
(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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17 pages, 5136 KiB  
Article
A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
by Shahbaz Sikandar, Rabbia Mahum and AbdulMalik Alsalman
Appl. Sci. 2023, 13(7), 4581; https://doi.org/10.3390/app13074581 - 04 Apr 2023
Cited by 7 | Viewed by 3275
Abstract
The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations [...] Read more.
The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention. Full article
(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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