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  • Open Access

    ARTICLE

    Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

    Qunyue Mu1,2, Qiancheng Yu1,2,*, Chengchen Zhou1,2, Lei Liu1,2, Xulong Yu1,2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 449-466, 2024, DOI:10.32604/cmc.2024.051728

    Abstract Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The More >

  • Open Access

    ARTICLE

    MG-YOLOv5s: A Faster and Stronger Helmet Detection Algorithm

    Zerui Xiao, Wei Liu, Zhiwei Ye*, Jiatang Yuan, Shishi Liu

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475

    Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

  • Open Access

    ARTICLE

    Joint Rain Streaks & Haze Removal Network for Object Detection

    Ragini Thatikonda1, Prakash Kodali1,*, Ramalingaswamy Cheruku2, Eswaramoorthy K.V3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844

    Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >

  • Open Access

    ARTICLE

    Probability-Enhanced Anchor-Free Detector for Remote-Sensing Object Detection

    Chengcheng Fan1,2,*, Zhiruo Fang3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710

    Abstract Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the More >

  • Open Access

    ARTICLE

    Real-Time Object Detection and Face Recognition Application for the Visually Impaired

    Karshiev Sanjar1, Soyoun Bang1, Sookhee Ryue2, Heechul Jung1,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3569-3583, 2024, DOI:10.32604/cmc.2024.048312

    Abstract The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types… More >

  • Open Access

    ARTICLE

    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

  • Open Access

    ARTICLE

    Enhanced Object Detection and Classification via Multi-Method Fusion

    Muhammad Waqas Ahmed1, Nouf Abdullah Almujally2, Abdulwahab Alazeb3, Asaad Algarni4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3315-3331, 2024, DOI:10.32604/cmc.2024.046501

    Abstract Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality. Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, and limited labeled data. To address these challenges, we introduce a comprehensive methodology to enhance image classification and object detection accuracy. The proposed approach involves the integration of multiple methods in a complementary way. The process commences with the application of Gaussian filters to mitigate the impact of noise interference. These images are then processed for segmentation using Fuzzy C-Means segmentation in parallel with saliency mapping techniques to find… More >

  • Open Access

    ARTICLE

    A Novel Foreign Object Detection Method in Transmission Lines Based on Improved YOLOv8n

    Yakui Liu1,2,3,*, Xing Jiang1, Ruikang Xu1, Yihao Cui1, Chenhui Yu1, Jingqi Yang1, Jishuai Zhou1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1263-1279, 2024, DOI:10.32604/cmc.2024.048864

    Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth… More >

  • Open Access

    ARTICLE

    A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

    Meng Huang, Honglei Wei*, Xianyi Zhai

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510

    Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution… More >

  • Open Access

    ARTICLE

    YOLOv5ST: A Lightweight and Fast Scene Text Detector

    Yiwei Liu1, Yingnan Zhao1,*, Yi Chen1, Zheng Hu1, Min Xia2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901

    Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place More >

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