Lieping Zhang^1,2, Hao Ma¹, Jiancheng Huang³, Cui Zhang^4,*, 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 >

Dawei Xu^1,2, Fan Huang¹, Jiaxin Zhang¹, Yunfang Liang¹, Baokun Zheng^3,*, 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 >

Meng Zhu^1,2, Xingyue Wang³, Honge Ren^3,4,*, Abeer Hakeem⁵, Linda Mohaisen^5,*

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 >

Honglin Wang¹, Yaolong Zhang^2,*, 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 >

Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, 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 >

Hongyang Wang^1,2, Yichen Shi³, Jun Feng^1,2,*, 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 >

Jim Wong^1,2, Zhaoxiang Zang^3,*

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 >

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 >

Pratik Jadhav¹, Vuppala Adithya Sairam¹, Niranjan Bhojane¹, Abhyuday Singh¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, 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 >

Min Yao^1,*, 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 >