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  • Open Access

    ARTICLE

    A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

    Zhong Qu1,*, Guoqing Mu1, Bin Yuan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 255-273, 2024, DOI:10.32604/cmes.2024.048175

    Abstract Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules… More > Graphic Abstract

    A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

  • Open Access

    ARTICLE

    Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

    Mahmood A. Mahmood1,2,*, Khalaf Alsalem1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

    Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is augmented using techniques such as… More >

  • Open Access

    ARTICLE

    Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

    Rui Wang1, Yao Zhou3,*, Guangchun Luo1, Peng Chen2, Dezhong Peng3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3011-3027, 2024, DOI:10.32604/cmes.2023.047065

    Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

  • Open Access

    ARTICLE

    Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

    Yuchun Li1,4, Mengxing Huang1,*, Yu Zhang2, Zhiming Bai3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1649-1668, 2024, DOI:10.32604/cmc.2023.046883

    Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the… More >

  • Open Access

    ARTICLE

    SDH-FCOS: An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

    Bin Zhou, Bo Li*, Wenfei Lan, Congwen Tian, Wei Yao

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 633-652, 2024, DOI:10.32604/cmc.2023.046667

    Abstract Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the… More >

  • Open Access

    ARTICLE

    Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

    Zahid Farooq Khan1, Muhammad Ramzan1,*, Mudassar Raza1, Muhammad Attique Khan2,3, Khalid Iqbal4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

    Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

  • Open Access

    ARTICLE

    YOLO-DD: Improved YOLOv5 for Defect Detection

    Jinhai Wang1, Wei Wang1, Zongyin Zhang1, Xuemin Lin1, Jingxian Zhao1, Mingyou Chen1, Lufeng Luo2,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 759-780, 2024, DOI:10.32604/cmc.2023.041600

    Abstract As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different… More >

  • Open Access

    ARTICLE

    Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

    Zheng Shi, Wanru Song*, Junhao Shan, Feng Liu

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849

    Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature… More >

  • Open Access

    ARTICLE

    Bearing Fault Diagnosis with DDCNN Based on Intelligent Feature Fusion Strategy in Strong Noise

    Chaoqian He1,2, Runfang Hao1,2,*, Kun Yang1,2, Zhongyun Yuan1,2, Shengbo Sang1,2, Xiaorui Wang1,2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3423-3442, 2023, DOI:10.32604/cmc.2023.045718

    Abstract Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology. However, conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data. Therefore, this paper proposes a dual-channel convolutional neural network (DDCNN) model that leverages dual data inputs. The DDCNN model introduces two key improvements. Firstly, one of the channels substitutes its convolution with a larger kernel, simplifying the structure while addressing the lack of global information and shallow features. Secondly, the feature layer combines data from different sensors based on their… More >

  • Open Access

    ARTICLE

    Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

    Ting Zhao1, Weiping Ding1, Haibo Huang1, Yudong Wu1,2,*

    Sound & Vibration, Vol.57, pp. 133-153, 2023, DOI:10.32604/sv.2023.044203

    Abstract The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal noise, this research presents a… More > Graphic Abstract

    Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

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