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Search Results (24)
  • Open Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876

    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and… More >

  • Open Access

    ARTICLE

    Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

    Shaofan Su1, Deyu Yuan2,*, Yuanxin Wang2, Meng Ding3

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 85-97, 2023, DOI:10.32604/csse.2023.030849

    Abstract With the rapid development of Internet technology, the type of information in the Internet is extremely complex, and a large number of riot contents containing bloody, violent and riotous components have appeared. These contents pose a great threat to the network ecology and national security. As a result, the importance of monitoring riotous Internet activity cannot be overstated. Convolutional Neural Network (CNN-based) target detection algorithm has great potential in identifying rioters, so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5 (YOLOv5), and further optimization of hyperparameters using genetic algorithm to… More >

  • Open Access

    ARTICLE

    An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model

    G. Naveen Sundar1, Stalin Selvaraj2, D. Narmadha1, K. Martin Sagayam3, A. Amir Anton Jone3, Ayman A. Aly4, Dac-Nhuong Le5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 31-46, 2022, DOI:10.32604/cmes.2022.019914

    Abstract Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research, we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target protein after assessing the top… More >

  • Open Access

    ARTICLE

    An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

    Pingping Yu1, Hongda Wang1, Xiaodong Zhao1,*, Guangchen Ruan2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4261-4276, 2022, DOI:10.32604/cmc.2022.029239

    Abstract Aiming at the problems of low target image resolution, insufficient target feature extraction, low detection accuracy and poor real time in remote engineering vehicle detection, an improved CenterNet target detection model is proposed in this paper. Firstly, EfficientNet-B0 with Efficient Channel Attention (ECA) module is used as the basic network, which increases the quality and speed of feature extraction and reduces the number of model parameters. Then, the proposed Adaptive Fusion Bidirectional Feature Pyramid Network (AF-BiFPN) module is applied to fuse the features of different feature layers. Furthermore, the feature information of engineering vehicle targets is added by making full… More >

  • Open Access

    ARTICLE

    Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

    T. Jagadesh1,2, B. Sheela Rani3,*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 449-463, 2023, DOI:10.32604/iasc.2023.026868

    Abstract Interference is a key factor in radar return misdetection. Strong interference might make it difficult to detect the signal or targets. When interference occurs in the sidelobes of the antenna pattern, Sidelobe Cancellation (SLC) and Sidelobe Blanking are two unique solutions to solve this problem (SLB). Aside from this approach, the probability of false alert and likelihood of detection are the most essential parameters in radar. The chance of a false alarm for any radar system should be minimal, and as a result, the probability of detection should be high. There are several interference cancellation strategies in the literature that… More >

  • Open Access

    ARTICLE

    LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

    Chengfan Li1,2, Lan Liu3,*, Junjuan Zhao1, Xuefeng Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202

    Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

  • Open Access

    ARTICLE

    Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

    Abdulsamad A. AL-Marghilani*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 809-824, 2022, DOI:10.32604/cmc.2022.021185

    Abstract Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect… More >

  • Open Access

    ARTICLE

    Multi-UAV Cooperative GPS Spoofing Based on YOLO Nano

    Yongjie Ding1, Zhangjie Fu1,2,*

    Journal of Cyber Security, Vol.3, No.2, pp. 69-78, 2021, DOI:10.32604/jcs.2021.019105

    Abstract In recent years, with the rapid development of the drone industry, drones have been widely used in many fields such as aerial photography, plant protection, performance, and monitoring. To effectively control the unauthorized flight of drones, using GPS spoofing attacks to interfere with the flight of drones is a relatively simple and highly feasible attack method. However, the current method uses ground equipment to carry out spoofing attacks. The attack range is limited and the flexibility is not high. Based on the existing methods, this paper proposes a multi-UAV coordinated GPS spoofing scheme based on YOLO Nano, which can launch… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Dynamic Target Detection and Tracking Based on Quantum Illumination LIDAR

    Qinghai Li1, Ziyi Zhao2, Hao Wu1,*, Xiaoyu Li3,*, Qinsheng Zhu1, Shan Yang4

    Journal of Quantum Computing, Vol.3, No.1, pp. 35-43, 2021, DOI:10.32604/jqc.2021.016634

    Abstract In the detection process of classic radars such as radar/lidar, the detection performance will be weakened due to the presence of background noise and loss. The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance, even in the presence of loss and noise. Based on this quantum illumination LIDAR, a theoretic scheme is developed for the detection and tracking of moving targets, and the trajectory of the object is analyzed. Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion (SPDC), an opaque target can be identified from the… More >

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