Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Jingjing Yan¹, Xuyang Zhuang^2,*, Xuezhuan Zhao^1,2, Xiaoyan Shao^1,*, Jiaqi Han¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5363-5386, 2025, DOI:10.32604/cmc.2025.059709 - 06 March 2025

Abstract The key to the success of few-shot semantic segmentation (FSS) depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set. Due to the few samples in the support set, FSS faces challenges such as intra-class differences, background (BG) mismatches between query and support sets, and ambiguous segmentation between the foreground (FG) and BG in the query set. To address these issues, The paper propose a multi-module network called CAMSNet, which includes four modules: the General Information Module (GIM), the Class Activation Map Aggregation (CAMA) module, the… More >

Xueli Shen, Meng Wang^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025

Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

Thai-Viet Dang^1,*, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025

Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

Ziqiang Tang¹, Chao Han¹, Hongwu Li¹, Zhou Fan¹, Ke Sun¹, Yuntian Huang¹, Yuhang Chen², Chenxing Wang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2813-2829, 2025, DOI:10.32604/cmc.2024.059094 - 17 February 2025

Abstract Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask… More >

Fengju Zhang¹, Kai Zhu^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2353-2372, 2025, DOI:10.32604/cmc.2024.058944 - 17 February 2025

Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >

Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Weiyu Hao¹, Jingyi Wang², Huimin Lu^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4419-4433, 2024, DOI:10.32604/cmc.2024.055478 - 19 December 2024

Abstract While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental… More >

Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2629-2642, 2024, DOI:10.32604/cmc.2024.056038 - 18 November 2024

Abstract Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception… More >

Jing Wang^1,2,*, Chen Zhang¹, Tianwen Lin¹

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1907-1925, 2024, DOI:10.32604/cmc.2024.052597 - 15 August 2024

Abstract When existing deep learning models are used for road extraction tasks from high-resolution images, they are easily affected by noise factors such as tree and building occlusion and complex backgrounds, resulting in incomplete road extraction and low accuracy. We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt. Then, ConvNeXt is used as the backbone network, which cooperates with the perceptual analysis network UPerNet, retains the detection head of the semantic segmentation, and builds a new model ConvNeXt-UPerNet to suppress noise interference. Training on the open-source DeepGlobe and CHN6-CUG… More >