Fangjun Luan^1,2,3, Xuewen Mu^1,2,3, Shuai Yuan^1,2,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2024.048502

Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries. However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To address these issues, we propose a novel approach for online signature verification, using a one-dimensional Ghost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature information and extracting local information, effectively complementing whole and local signature features and mitigating… More >

Xiaoqin Wu, Songrong Qian^*, Ming Yang

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3349-3366, 2023, DOI:10.32604/cmc.2023.043671

Abstract Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been realized for management, but the… More >

Jun Wu, Penghui Fan, Yingxin Sun, Weifeng Gui^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1305-1321, 2023, DOI:10.32604/cmes.2022.020919

Abstract Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and… More >

Yan Jiang¹, Xinrui Jia¹, Liguo Zhang^1,2,*, Ye Yuan¹, Lei Chen³, Guisheng Yin¹

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4051-4067, 2021, DOI:10.32604/cmc.2021.016481

Abstract The technology for image-to-image style transfer (a prevalent image processing task) has developed rapidly. The purpose of style transfer is to extract a texture from the source image domain and transfer it to the target image domain using a deep neural network. However, the existing methods typically have a large computational cost. To achieve efficient style transfer, we introduce a novel Ghost module into the GANILLA architecture to produce more feature maps from cheap operations. Then we utilize an attention mechanism to transform images with various styles. We optimize the original generative adversarial network (GAN) by using more efficient calculation… More >