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  • Open Access


    A Deep Learning Model of Traffic Signs in Panoramic Images Detection

    Kha Tu Huynh1, Thi Phuong Linh Le1, Muhammad Arif2, Thien Khai Tran3,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 401-418, 2023, DOI:10.32604/iasc.2023.036981

    Abstract To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in More >

  • Open Access


    Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network

    Yunyoung Nam1, Seong Jun Choi2, Jihwan Shin1, Jinseok Lee3,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1521-1532, 2023, DOI:10.32604/csse.2023.034192

    Abstract An otoscope is traditionally used to examine the eardrum and ear canal. A diagnosis of otitis media (OM) relies on the experience of clinicians. If an examiner lacks experience, the examination may be difficult and time-consuming. This paper presents an ear disease classification method using middle ear images based on a convolutional neural network (CNN). Especially the segmentation and classification networks are used to classify an otoscopic image into six classes: normal, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM), congenital cholesteatoma (CC) and traumatic perforations (TMPs). The Mask R-CNN More >

  • Open Access


    DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection

    Jian Peng1,2, Yifang Zhao1,2, Dengyong Zhang1,2,*, Feng Li1,2, Arun Kumar Sangaiah3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3405-3419, 2023, DOI:10.32604/cmc.2023.027627

    Abstract Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages… More >

  • Open Access


    A Deep Learning-Based Approach for Road Surface Damage Detection

    Bakhytzhan Kulambayev1,*, Gulbakhram Beissenova2,3, Nazbek Katayev4, Bayan Abduraimova5, Lyazzat Zhaidakbayeva2, Alua Sarbassova6, Oxana Akhmetova7, Sapar Issayev4, Laura Suleimenova8, Syrym Kasenov6, Kunsulu Shadinova9, Abay Shyrakbaev10

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3403-3418, 2022, DOI:10.32604/cmc.2022.029544

    Abstract Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road accidents. Currently, various methods of photo and video surveillance are used to monitor the condition of the road surface. The manual approach to evaluation and analysis of the received data can take a protracted period of time. Thus, it is necessary to improve the procedures for inspection and assessment of the condition of control objects with the help of computer vision and deep learning techniques. In this paper, we propose a model based on Mask Region-based Convolutional… More >

  • Open Access


    Wall Cracks Detection in Aerial Images Using Improved Mask R-CNN

    Wei Chen1, Caoyang Chen1,*, Mi Liu1, Xuhong Zhou2, Haozhi Tan3, Mingliang Zhang4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 767-782, 2022, DOI:10.32604/cmc.2022.028571

    Abstract The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN… More >

  • Open Access


    Object Detection for Cargo Unloading System Based on Fuzzy C Means

    Sunwoo Hwang1, Jaemin Park1, Jongun Won2, Yongjang Kwon3, Youngmin Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4167-4181, 2022, DOI:10.32604/cmc.2022.023295

    Abstract With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to More >

  • Open Access


    Identification of Anomalous Behavioral Patterns in Crowd Scenes

    Muhammad Asif Nauman*, Muhammad Shoaib

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 925-939, 2022, DOI:10.32604/cmc.2022.022147

    Abstract Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained… More >

  • Open Access


    Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm

    Jiaxiu Dong1,2,3, Jianhua Liu4, Niannian Wang1,2,3,*, Hongyuan Fang1,2,3, Jinping Zhang1, Haobang Hu1,2,3, Duo Ma1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 541-564, 2021, DOI:10.32604/cmes.2021.015875

    Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a… More >

  • Open Access


    Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

    Qiqiang Chen1, *, Xinxin Gan2, Wei Huang1, Jingjing Feng1, H. Shim3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191

    Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales More >

  • Open Access


    A Novel Steganography Algorithm Based on Instance Segmentation

    Ruohan Meng1, 2, Qi Cui1, 2, Zhili Zhou1, 2, Chengsheng Yuan1, 2, 3, Xingming Sun1, 2, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 183-196, 2020, DOI:10.32604/cmc.2020.05317

    Abstract Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion. We take advantage of the complexity of the object texture and consider that under certain circumstances, the object texture is more complex than the background of the image, so the foreground object is more suitable for steganography than the background. On the basis of instance segmentation, such as Mask R-CNN, the proposed method hides secret information into each More >

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