Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025

Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >

Rongrong Gong^1,#, Tingxian Zhang^2,#, Yawen Wei², Dengyong Zhang², Yan Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1221-1234, 2025, DOI:10.32604/cmc.2025.066842 - 29 August 2025

Abstract Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions, which is a typical manifestation of this trend. With the increasing complexity of image in tasks and the growth of data scale, existing deep learning methods still have some limitations. For example, they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal. To solve this problem, the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network. The encoder of the proposed model More >

Xiaosha Qi¹, Genlin Ji^2,*, Jie Zhang², Bo Sheng³

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.027182 - 24 March 2022

Abstract Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE).… More >

Linfang Yu, Zhen Qin^*, Yi Ding, Zhiguang Qin

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 805-828, 2021, DOI:10.32604/cmes.2021.017332 - 08 October 2021

Abstract As an important part of the new generation of information technology, the Internet of Things (IoT) has been widely concerned and regarded as an enabling technology of the next generation of health care system. The fundus photography equipment is connected to the cloud platform through the IoT, so as to realize the real-time uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence. At the same time, important security and privacy issues have emerged. The data uploaded to the cloud platform involves more personal attributes, health status and medical application data… More >

Chung-Il Kim¹, Jehyeok Rew², Yongjang Cho², Eenjun Hwang^2,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633 - 06 May 2021

Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel More >

Runzhe Tao^{1, *}, Yonghong Zhang¹, Lihua Wang¹, Pengyan Cai¹, Haowen Tan²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526 - 16 September 2020

Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution… More >

Yecai Guo^1,2,*, Chen Li^1,2, Qi Liu³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 829-843, 2019, DOI:10.32604/cmc.2019.03729

Abstract Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R²N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping More >