Special lssues
Table of Content

Machine Vision Detection and Intelligent Recognition

Submission Deadline: 31 December 2023 (closed)

Guest Editors

Prof. Kechen Song, Northeastern University, China.
Prof. Shaopeng Hu, Hiroshima University, Japan.
Dr. Xin Wen, Shenyang University of Technology, China.

Summary

Machine vision detection and intelligent recognition are important research areas in computer vision with wide-ranging applications in manufacturing, healthcare, security, transportation, robotics, industrial production, aerospace, and many other industries. Machine vision detection involves capturing visual information using cameras or sensors and then using techniques such as image processing and machine learning to extract relevant features or identify defects in manufacturing processes. Intelligent recognition involves using algorithms to identify and classify objects or patterns in images or videos, enabling automated decision-making in various applications.

 

Recent advancements in these areas have been driven by the acceleration of algorithms such as image processing and deep learning, as well as hardware such as CPUs and GPUs. This has made machine vision detection and recognition more accurate and efficient, particularly in areas such as defect detection, industrial monitoring, high-speed target detection, 3D measurement, intelligent recognition, and so on.

 

However, to make machine vision detection and intelligent recognition technologies more reliable and effective in practical applications, several challenges still need to be addressed. These include how to process massive amounts of image or video data, improve real-time processing, ensure data privacy and security (e.g., in healthcare and surveillance), increase detection and recognition accuracy in complex environments, and enhance the interpretability of deep learning algorithms. Addressing these challenges will be crucial to enable the widespread adoption of machine vision detection and intelligent recognition technologies in various applications.

 

Overall, machine vision detection and intelligent recognition have the potential to revolutionize many industries and are an active area of research in computer vision. This special issue provides a platform for researchers and practitioners to share their latest findings and insights in machine vision detection and intelligent recognition. The special issue welcomes original research articles and review articles that report on the latest advancements and challenges in this field. The topics of interest for this special issue include, but are not limited to:

 

· Object detection and tracking

· Deep learning and pattern recognition

· Defect detection and segmentation

· 3D measurement and reconstruction

· Surveillance and security using machine vision

· Human-computer interaction

· Visual inspection and monitoring

· High-speed vision

· Image segmentation and classification

· Real-time image processing

· Remote monitoring and control of industrial processes

· Multiple camera or sensor systems

· Scene understanding and activity recognition

· Autonomous driving and obstacle detection

· Simultaneous localization and mapping

· Visual servoing and control of robots

· Evaluation and benchmarking of algorithm or system

· Challenges and future directions


Keywords

Computer Vision, Machine Learning, Intelligent Detection, Image Processing, Intelligent Recognition, Deep Learning, Robotics, High-speed Vision, 3D Measurement

Published Papers


  • Open Access

    ARTICLE

    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery analysis. Specifically, we emphasize the… More >

  • Open Access

    ARTICLE

    Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

    Husam Ahmad Al Hamad, Mohammad Shehab
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2015-2034, 2024, DOI:10.32604/cmc.2024.048527
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Recognizing handwritten characters remains a critical and formidable challenge within the realm of computer vision. Although considerable strides have been made in enhancing English handwritten character recognition through various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexity arises from the diverse array of writing styles among individuals, coupled with the various shapes that a single character can take when positioned differently within document images, rendering the task more perplexing. In this study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locate the local minima of the vertical and diagonal word image densities… More >

  • Open Access

    ARTICLE

    Enhanced Object Detection and Classification via Multi-Method Fusion

    Muhammad Waqas Ahmed, Nouf Abdullah Almujally, Abdulwahab Alazeb, Asaad Algarni, Jeongmin Park
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3315-3331, 2024, DOI:10.32604/cmc.2024.046501
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality. Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, and limited labeled data. To address these challenges, we introduce a comprehensive methodology to enhance image classification and object detection accuracy. The proposed approach involves the integration of multiple methods in a complementary way. The process commences with the application of Gaussian filters to mitigate the impact of noise interference. These images are then processed for segmentation using Fuzzy C-Means segmentation in parallel with saliency mapping techniques to find the most prominent regions. The… More >

  • Open Access

    ARTICLE

    U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

    Ananthakrishnan Balasundaram, Ayesha Shaik, Japmann Kaur Banga, Aman Kumar Singh
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

  • Open Access

    ARTICLE

    Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

    Chunming Wu, Wukai Liu, Xin Ma
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1441-1461, 2024, DOI:10.32604/cmc.2024.048136
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… More >

  • Open Access

    ARTICLE

    HgaNets: Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

    Wuyan Liang, Xiaolong Xu
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >

  • Open Access

    ARTICLE

    A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

    Xiao Lu, Chengling Jiang, Zhoujun Ma, Haitao Li, Yuexin Liu
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect tiny objects. Secondly, a simplified… More >

  • Open Access

    ARTICLE

    Unmanned Aerial Vehicles General Aerial Person-Vehicle Recognition Based on Improved YOLOv8s Algorithm

    Zhijian Liu
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction module (SDCN). Moreover, the model… More >

  • Open Access

    ARTICLE

    SAM Era: Can It Segment Any Industrial Surface Defects?

    Kechen Song, Wenqi Cui, Han Yu, Xingjie Li, Yunhui Yan
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel surface defects, tile surface defects,… More >

  • Open Access

    ARTICLE

    Unmanned Ship Identification Based on Improved YOLOv8s Algorithm

    Chun-Ming Wu, Jin Lei, Wu-Kai Liu, Mei-Ling Ren, Ling-Li Ran
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference. The algorithm… More >

  • Open Access

    REVIEW

    A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

    Usman Khan, Muhammad Khalid Khan, Muhammad Ayub Latif, Muhammad Naveed, Muhammad Mansoor Alam, Salman A. Khan, Mazliham Mohd Su’ud
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

  • Open Access

    ARTICLE

    Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms

    Yan Li, Qiyuan Wang, Kaidi Jia
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

  • Open Access

    ARTICLE

    MDCN: Modified Dense Convolution Network Based Disease Classification in Mango Leaves

    Chirag Chandrashekar, K. P. Vijayakumar, K. Pradeep, A. Balasundaram
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

  • Open Access

    ARTICLE

    An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

    Liqiu Ren, Zhanying Li, Xueyu He, Lingyan Kong, Yinghao Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2829-2845, 2024, DOI:10.32604/cmc.2024.047028
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the… More >

  • Open Access

    ARTICLE

    Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

    Chengjun Wang, Fan Ding, Yiwen Wang, Renyuan Wu, Xingyu Yao, Chengjie Jiang, Liuyi Ling
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided context-feature pyramid network (AC-FPN) is… More >

  • Open Access

    ARTICLE

    Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

    Xinyu Hu, Defeng Kong, Xiyang Liu, Junwei Zhang, Daode Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet… More >

  • Open Access

    ARTICLE

    A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

    Yaoyao Du, Xiangkui Jiang
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 303-327, 2024, DOI:10.32604/cmc.2023.046068
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the… More >

  • Open Access

    ARTICLE

    A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

    Tianmin Deng, Xiyue Zhang, Xinxin Cheng
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network, which amplifies the disparities between… More >

  • Open Access

    ARTICLE

    Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

    Lei Hu, Yuanwen Lu, Si Wang, Wenbin Wang, Yongmei Zhang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2735-2749, 2023, DOI:10.32604/cmc.2023.042974
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle (UAV) due to the complex background of distribution lines, variable morphology of equipment, and large differences in equipment sizes. Therefore, aiming at the difficult detection of power equipment in UAV inspection images, we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s. Based on the YOLOx-s network, we make the following improvements: 1) The Receptive Field Block (RFB) module is added after the shallow feature layer of the backbone network to… More >

  • Open Access

    ARTICLE

    An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

    Chao Dong, Xiangkui Jiang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the Neck part of the original… More >

  • Open Access

    ARTICLE

    Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

    Ameerah Abdullah Alshahrani, Emad Sami Jaha, Nahed Alowidi
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3489-3509, 2023, DOI:10.32604/cmc.2023.044490
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various real-life applications, such as helping crime investigators… More >

  • Open Access

    ARTICLE

    C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

    Xiujuan Wang, Yiqi Tian, Kangfeng Zheng, Chutong Liu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1949-1965, 2023, DOI:10.32604/cmc.2023.042224
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Rapid advancement of intelligent transportation systems (ITS) and autonomous driving (AD) have shown the importance of accurate and efficient detection of traffic signs. However, certain drawbacks, such as balancing accuracy and real-time performance, hinder the deployment of traffic sign detection algorithms in ITS and AD domains. In this study, a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed. An enhanced backbone network module, called C2Net, which uses an upgraded bidirectional Res2Net, was introduced to mitigate information loss in the feature extraction process and to achieve information… More >

  • Open Access

    ARTICLE

    Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

    Shorouq Alshawabkeh, Li Wu, Daojun Dong, Yao Cheng, Liping Li, Mohammad Alanaqreh
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 63-77, 2023, DOI:10.32604/cmc.2023.042183
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC). Our findings reveal that Model… More >

  • Open Access

    ARTICLE

    Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

    Wenkai Qin, Tianliang Lu, Lu Zhang, Shufan Peng, Da Wan
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >

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