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  • Open Access


    Leaky Cable Fixture Detection in Railway Tunnel Based on RW DCGAN and Compressed GS-YOLOv5

    Suhang Li1, Yunzuo Zhang1,*, Ruixue Liu2, Jiayu Zhang1, Zhouchen Song1, Yutai Wang1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1163-1180, 2023, DOI:10.32604/iasc.2023.037902

    Abstract The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures. To ensure safety, checking the regular leaky cable fixture is necessary to eliminate the potential danger. At present, the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time. The faulty fixture is also insufficient and difficult to obtain, seriously affecting the model detection effect. To solve these problems, an innovative detection method is proposed in this paper. Firstly, we presented the Res-Net and Wasserstein-Deep Convolution GAN (RW-DCGAN)… More >

  • Open Access


    Fire Detection Algorithm Based on an Improved Strategy of YOLOv5 and Flame Threshold Segmentation

    Yuchen Zhao, Shulei Wu*, Yaoru Wang, Huandong Chen*, Xianyao Zhang, Hongwei Zhao

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5639-5657, 2023, DOI:10.32604/cmc.2023.037829

    Abstract Due to the rapid growth and spread of fire, it poses a major threat to human life and property. Timely use of fire detection technology can reduce disaster losses. The traditional threshold segmentation method is unstable, and the flame recognition methods of deep learning require a large amount of labeled data for training. In order to solve these problems, this paper proposes a new method combining You Only Look Once version 5 (YOLOv5) network model and improved flame segmentation algorithm. On the basis of the traditional color space threshold segmentation method, the original segmentation threshold is replaced by the proportion… More >

  • Open Access


    A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s

    Hongyu Lin, Feng Jiang*, Yu Jiang, Huiyin Luo, Jian Yao, Jiaxin Liu

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5321-5336, 2023, DOI:10.32604/cmc.2023.036893

    Abstract Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of the network to get more… More >

  • Open Access


    Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1847-1861, 2023, DOI:10.32604/cmc.2023.035698

    Abstract Deep learning has been constantly improving in recent years, and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, the detection of such defects is key to the production of high-quality products. Moreover, surface defects of… More >

  • Open Access


    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


    An Elevator Button Recognition Method Combining YOLOv5 and OCR

    Xinliang Tang1, Caixing Wang1, Jingfang Su1,*, Cecilia Taylor2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 117-131, 2023, DOI:10.32604/cmc.2023.033327

    Abstract Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in… More >

  • Open Access


    A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

    Qunbiao Wu1, Zhen Wang1,*, Haifeng Fang1, Junji Chen1, Xinfeng Wan2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 961-979, 2023, DOI:10.32604/csse.2023.036239

    Abstract For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump… More >

  • Open Access


    Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

    Jakkrit Suttanuruk1, Sajjakaj Jomnonkwao1,*, Vatanavong Ratanavaraha1, Sarunya Kanjanawattana2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5573-5588, 2023, DOI:10.32604/cmc.2023.033900

    Abstract Thailand has been on the World Health Organization (WHO)’s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television… More >

  • Open Access


    Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

    Md Zahid Hasan Ontor1, Md Mamun Ali1, Kawsar Ahmed2,3,*, Francis M. Bui3, Fahad Ahmed Al-Zahrani4, S. M. Hasan Mahmud5, Sami Azam6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3727-3741, 2023, DOI:10.32604/cmc.2023.032794

    Abstract Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix… More >

  • Open Access


    Robust Vehicle Detection Based on Improved You Look Only Once

    Sunil Kumar1, Manisha Jailia1, Sudeep Varshney2, Nitish Pathak3, Shabana Urooj4,*, Nouf Abd Elmunim4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3561-3577, 2023, DOI:10.32604/cmc.2023.029999

    Abstract Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets… More >

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