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  • Open Access


    Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

    Wenshun Sheng*, Xiongfeng Yu, Jiayan Lin, Xin Chen

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1549-1569, 2023, DOI:10.32604/csse.2023.039410

    Abstract Small targets and occluded targets will inevitably appear in the image during the shooting process due to the influence of angle, distance, complex scene, illumination intensity, and other factors. These targets have few effective pixels, few features, and no apparent features, which makes extracting their efficient features difficult and easily leads to false detection, missed detection, and repeated detection, affecting the performance of target detection models. An improved faster region convolutional neural network (RCNN) algorithm (CF-RCNN) integrating convolutional block attention module (CBAM) and feature pyramid networks (FPN) is proposed to improve the detection and recognition accuracy of small-size objects, occluded… More >

  • Open Access


    Lightweight Surface Litter Detection Algorithm Based on Improved YOLOv5s

    Zunliang Chen1,2, Chengxu Huang1,2, Lucheng Duan1,2, Baohua Tan1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451

    Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >

  • Open Access


    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

    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 results show that the model… More >

  • Open Access


    Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

    Rehman Ullah Khan1,*, Woei Sheng Wong1, Insaf Ullah2, Fahad Algarni3, Muhammad Inam Ul Haq4, Mohamad Hardyman bin Barawi1, Muhammad Asghar Khan2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2755-2772, 2022, DOI:10.32604/cmc.2022.022471

    Abstract The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos… More >

  • Open Access


    Feedback LSTM Network Based on Attention for Image Description Generator

    Zhaowei Qu1,*, Bingyu Cao1, Xiaoru Wang1, Fu Li2, Peirong Xu1, Luhan Zhang1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 575-589, 2019, DOI:10.32604/cmc.2019.05569

    Abstract Images are complex multimedia data which contain rich semantic information. Most of current image description generator algorithms only generate plain description, with the lack of distinction between primary and secondary object, leading to insufficient high-level semantic and accuracy under public evaluation criteria. The major issue is the lack of effective network on high-level semantic sentences generation, which contains detailed description for motion and state of the principal object. To address the issue, this paper proposes the Attention-based Feedback Long Short-Term Memory Network (AFLN). Based on existing codec framework, there are two independent sub tasks in our method: attention-based feedback LSTM… More >

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