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  • Open Access

    ARTICLE

    An Improved Lightweight Safety Helmet Detection Algorithm for YOLOv8

    Lieping Zhang1,2, Hao Ma1, Jiancheng Huang3, Cui Zhang4,*, Xiaolin Gao2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2245-2265, 2025, DOI:10.32604/cmc.2025.061519 - 16 April 2025

    Abstract Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.… More >

  • Open Access

    ARTICLE

    GMS: A Novel Method for Detecting Reentrancy Vulnerabilities in Smart Contracts

    Dawei Xu1,2, Fan Huang1, Jiaxin Zhang1, Yunfang Liang1, Baokun Zheng3,*, Jian Zhao1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2207-2220, 2025, DOI:10.32604/cmc.2025.061455 - 16 April 2025

    Abstract With the rapid proliferation of Internet of Things (IoT) devices, ensuring their communication security has become increasingly important. Blockchain and smart contract technologies, with their decentralized nature, provide strong security guarantees for IoT. However, at the same time, smart contracts themselves face numerous security challenges, among which reentrancy vulnerabilities are particularly prominent. Existing detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer functions. To address these limitations, this paper proposes a novel detection method that combines pattern matching… More >

  • Open Access

    ARTICLE

    Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

    Meng Zhu1,2, Xingyue Wang3, Honge Ren3,4,*, Abeer Hakeem5, Linda Mohaisen5,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2889-2905, 2025, DOI:10.32604/cmc.2025.061421 - 16 April 2025

    Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >

  • Open Access

    ARTICLE

    DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

    Honglin Wang1, Yaolong Zhang2,*, Cheng Zhu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

    Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

  • Open Access

    ARTICLE

    SA-ResNet: An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion

    Zengyu Cai1,*, Yuming Dai1, Jianwei Zhang2,3,*, Yuan Feng4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3335-3350, 2025, DOI:10.32604/cmc.2025.061206 - 16 April 2025

    Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >

  • Open Access

    ARTICLE

    PNSS: Unknown Face Presentation Attack Detection with Pseudo Negative Sample Synthesis

    Hongyang Wang1,2, Yichen Shi3, Jun Feng1,2,*, Zitong Yu4, Zhuofu Tao5

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3097-3112, 2025, DOI:10.32604/cmc.2025.061019 - 16 April 2025

    Abstract Face Presentation Attack Detection (fPAD) plays a vital role in securing face recognition systems against various presentation attacks. While supervised learning-based methods demonstrate effectiveness, they are prone to overfitting to known attack types and struggle to generalize to novel attack scenarios. Recent studies have explored formulating fPAD as an anomaly detection problem or one-class classification task, enabling the training of generalized models for unknown attack detection. However, conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown attacks. To address this challenge, we propose a novel framework focusing on… More >

  • Open Access

    ARTICLE

    DDT-Net: Deep Detail Tracking Network for Image Tampering Detection

    Jim Wong1,2, Zhaoxiang Zang3,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3451-3469, 2025, DOI:10.32604/cmc.2025.061006 - 16 April 2025

    Abstract In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail… More >

  • Open Access

    ARTICLE

    VPM-Net: Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling

    Haitao Xie, Yuliang Chen, Yunjie Zeng, Lingyu Yan, Zhizhi Wang, Zhiwei Ye*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3389-3410, 2025, DOI:10.32604/cmc.2025.060783 - 16 April 2025

    Abstract With the rapid development of intelligent video surveillance technology, pedestrian re-identification has become increasingly important in multi-camera surveillance systems. This technology plays a critical role in enhancing public safety. However, traditional methods typically process images and text separately, applying upstream models directly to downstream tasks. This approach significantly increases the complexity of model training and computational costs. Furthermore, the common class imbalance in existing training datasets limits model performance improvement. To address these challenges, we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling (VPM-Net). First, we… More >

  • Open Access

    ARTICLE

    Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder

    Pratik Jadhav1, Vuppala Adithya Sairam1, Niranjan Bhojane1, Abhyuday Singh1, Shilpa Gite1,2, Biswajeet Pradhan3,*, Mrinal Bachute1, Abdullah Alamri4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3493-3517, 2025, DOI:10.32604/cmc.2025.060764 - 16 April 2025

    Abstract Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time real-time. Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems. The low-cost thermal imaging software produces low-resolution thermal images in grayscale format, hence necessitating methods for improving the resolution and colorizing the images. The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images, followed by a sparse autoencoder for colorization of thermal images and a multimodal convolutional neural network for… More >

  • Open Access

    ARTICLE

    An Improved Knowledge Distillation Algorithm and Its Application to Object Detection

    Min Yao1,*, Guofeng Liu2, Yaozu Zhang3, Guangjie Hu1

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2189-2205, 2025, DOI:10.32604/cmc.2025.060609 - 16 April 2025

    Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >

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