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  • Open Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876 - 06 February 2023

    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection… More >

  • Open Access

    ARTICLE

    License Plate Recognition via Attention Mechanism

    Longjuan Wang1,2, Chunjie Cao1,2, Binghui Zou1,2, Jun Ye1,2,*, Jin Zhang3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1801-1814, 2023, DOI:10.32604/cmc.2023.032785 - 06 February 2023

    Abstract License plate recognition technology use widely in intelligent traffic management and control. Researchers have been committed to improving the speed and accuracy of license plate recognition for nearly 30 years. This paper is the first to propose combining the attention mechanism with YOLO-v5 and LPRnet to construct a new license plate recognition model (LPR-CBAM-Net). Through the attention mechanism CBAM (Convolutional Block Attention Module), the importance of different feature channels in license plate recognition can be re-calibrated to obtain proper attention to features. Force information to achieve the purpose of improving recognition speed and accuracy. Experimental More >

  • Open Access

    ARTICLE

    Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

    Samer Abdulateef Waheeb*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 981-998, 2023, DOI:10.32604/csse.2023.035753 - 20 January 2023

    Abstract Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL),… More >

  • Open Access

    ARTICLE

    3D Object Detection with Attention: Shell-Based Modeling

    Xiaorui Zhang1,2,3,4,*, Ziquan Zhao1, Wei Sun4,5, Qi Cui6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230 - 20 January 2023

    Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate… More >

  • Open Access

    ARTICLE

    Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net

    Chuanlong Sun, Hong Zhao*, Liang Mu, Fuliang Xu, Laiwei Lu

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 787-801, 2023, DOI:10.32604/cmes.2023.025119 - 05 January 2023

    Abstract Image semantic segmentation has become an essential part of autonomous driving. To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed. Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network. To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test More >

  • Open Access

    ARTICLE

    Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion

    Jianwei Yuan1, Xinli Song1,*, Huaijian Pu2, Zhixiong Zheng3, Ziyang Niu3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6485-6503, 2023, DOI:10.32604/cmc.2023.035165 - 28 December 2022

    Abstract Regular inspection of bridge cracks is crucial to bridge maintenance and repair. The traditional manual crack detection methods are time-consuming, dangerous and subjective. At the same time, for the existing mainstream vision-based automatic crack detection algorithms, it is challenging to detect fine cracks and balance the detection accuracy and speed. Therefore, this paper proposes a new bridge crack segmentation method based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+ network framework. First, the improved lightweight MobileNet-v2 network and dilated separable convolution are integrated into the original DeeplabV3+ network to improve… More >

  • Open Access

    ARTICLE

    Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

    Muhammad Ibrahim1, Imran Sarwar Bajwa1, Nadeem Sarwar2,*, Haroon Abdul Waheed3, Muhammad Zulkifl Hasan4, Muhammad Zunnurain Hussain4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5301-5317, 2023, DOI:10.32604/cmc.2023.032856 - 28 December 2022

    Abstract Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To overcome the cold start problem, the new overall rating is generated… More >

  • Open Access

    ARTICLE

    CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

    Shengchun Wang1, Baoxuan Huang1, Tsz Ho Wong2, Jingui Huang1,*, Hong Deng1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4667-4684, 2023, DOI:10.32604/cmc.2023.031444 - 28 December 2022

    Abstract In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features. A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality. In order… More >

  • Open Access

    ARTICLE

    Group Psychological Intervention for Children with Hyperactivity Disorder

    Ying Xu*

    International Journal of Mental Health Promotion, Vol.25, No.1, pp. 81-97, 2023, DOI:10.32604/ijmhp.2022.023720 - 29 November 2022

    Abstract ADHD is a broad psychiatric disorder that affects children of normal or near-normal intelligence. It is characterized by inattention, hyperactivity, and age-inappropriate impulsivity, and it is often accompanied by learning difficulties, behavioral, emotional, and interpersonal problems. On the other hand, hyperactive tendencies in children with ADHD exhibit ADHD-like behaviors such as lack of self-control, inattention, hyperactivity, and emotional impulsivity. However, because their symptoms are less severe, they do not meet the diagnostic criteria for ADHD but are ADHD or at risk of developing ADHD. The purpose of this study is to alleviate and reduce children’s More >

  • Open Access

    ARTICLE

    3D Vehicle Detection Algorithm Based on Multimodal Decision-Level Fusion

    Peicheng Shi1,*, Heng Qi1, Zhiqiang Liu1, Aixi Yang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2007-2023, 2023, DOI:10.32604/cmes.2023.022304 - 23 November 2022

    Abstract 3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving. The algorithm based on the Camera-LiDAR object candidate fusion method (CLOCs) is currently considered to be a more effective decision-level fusion algorithm, but it does not fully utilize the extracted features of 3D and 2D. Therefore, we proposed a 3D vehicle detection algorithm based on multimodal decision-level fusion. First, project the anchor point of the 3D detection bounding box into the 2D image, calculate the distance between 2D and 3D anchor points, and use this distance as a new… More > Graphic Abstract

    3D Vehicle Detection Algorithm Based on Multimodal Decision-Level Fusion

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