Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (11)
  • Open Access


    Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

    Shuja Ali1, Ahmad Jalal1, Mohammed Hamad Alatiyyah2, Khaled Alnowaiser3, Jeongmin Park4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114

    Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal of this study is to… More >

  • Open Access


    An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

    Mubashir Javaid1, Muazzam Maqsood2, Farhan Aadil2, Jibran Safdar1, Yongsung Kim3,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1295-1310, 2023, DOI:10.32604/iasc.2023.028262

    Abstract Currently, worldwide industries and communities are concerned with building, expanding, and exploring the assets and resources found in the oceans and seas. More precisely, to analyze a stock, archaeology, and surveillance, several cameras are installed underseas to collect videos. However, on the other hand, these large size videos require a lot of time and memory for their processing to extract relevant information. Hence, to automate this manual procedure of video assessment, an accurate and efficient automated system is a greater necessity. From this perspective, we intend to present a complete framework solution for the task of video summarization and object… More >

  • Open Access


    Image Captioning Using Detectors and Swarm Based Learning Approach for Word Embedding Vectors

    B. Lalitha1,*, V. Gomathi2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 173-189, 2023, DOI:10.32604/csse.2023.024118

    Abstract IC (Image Captioning) is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements. However, in existing works, because of the complexity in images, neglecting major relation between the object in an image, poor quality image, labelling it remains a big problem for researchers. Hence, the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC. So in this research work the main contribution deals with the framework consists of three phases that is image understanding, textual understanding and decoding. Initially, the image… More >

  • Open Access


    Deep Neural Network Based Vehicle Detection and Classification of Aerial Images

    Sandeep Kumar1, Arpit Jain2,*, Shilpa Rani3, Hammam Alshazly4, Sahar Ahmed Idris5, Sami Bourouis6

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 119-131, 2022, DOI:10.32604/iasc.2022.024812

    Abstract The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of… More >

  • Open Access


    A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

    Farman Ali1, Sadia Khan2, Arbab Waseem Abbas2, Babar Shah3, Tariq Hussain2, Dongho Song4,*, Shaker EI-Sappagh5,6, Jaiteg Singh7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103

    Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning… More >

  • Open Access


    Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets

    Zhaohui Bu1, Xuejun Zhang2,3,*, Jianxiang Lu4, Huan Lao5, Chan Liang2, Xianfu Xu2, Yini Wei2, Hongjie Zeng2

    Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 17-28, 2022, DOI:10.32604/mcb.2022.018318

    Abstract The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome… More >

  • Open Access


    An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision

    Jian Yang, Lang Xin#, Haihui Huang*,#, Qiang He

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 779-802, 2021, DOI:10.32604/cmes.2021.014993

    Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS)… More >

  • Open Access


    Real-Time Recognition and Location of Indoor Objects

    Jinxing Niu1,*, Qingsheng Hu1, Yi Niu1, Tao Zhang1, Sunil Kumar Jha2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2221-2229, 2021, DOI:10.32604/cmc.2021.017073

    Abstract Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera is used to obtain the… More >

  • Open Access


    HPMC: A Multi-target Tracking Algorithm for the IoT

    Xinyue Lv1, Xiaofeng Lian2,*, Li Tan1, Yanyan Song1, Chenyu Wang3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 513-526, 2021, DOI:10.32604/iasc.2021.016450

    Abstract With the rapid development of the Internet of Things and advanced sensors, vision-based monitoring and forecasting applications have been widely used. In the context of the Internet of Things, visual devices can be regarded as network perception nodes that perform complex tasks, such as real-time monitoring of road traffic flow, target detection, and multi-target tracking. We propose the High-Performance detection and Multi-Correlation measurement algorithm (HPMC) to address the problem of target occlusion and perform trajectory correlation matching for multi-target tracking. The algorithm consists of three modules: 1) For the detection module, we proposed the You Only Look Once(YOLO)v3_plus model, which… More >

  • Open Access


    YOLOv3 Attention Face Detector with High Accuracy and Efficiency

    Qiyuan Liu, Shuhua Lu*, Lingqiang Lan

    Computer Systems Science and Engineering, Vol.37, No.2, pp. 283-295, 2021, DOI:10.32604/csse.2021.014086

    Abstract In recent years, face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision. However, the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied. In this paper, using Darknet-53 as backbone, we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency. The attention mechanism is introduced to enhance much higher discrimination of the deep features, and the trick of data augmentation is used in the training… More >

Displaying 1-10 on page 1 of 11. Per Page