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  • Open Access


    Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

    Dongmei Chen1, Peipei Cao1, Lijie Yan1, Huidong Chen1, Jia Lin1, Xin Li2, Lin Yuan3, Kaihua Wu1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 261-275, 2024, DOI:10.32604/phyton.2024.046331

    Abstract Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea. Traditional tea-picking machines may compromise the quality of the tea leaves. High-quality teas are often handpicked and need more delicate operations in intelligent picking machines. Compared with traditional image processing techniques, deep learning models have stronger feature extraction capabilities, and better generalization and are more suitable for practical tea shoot harvesting. However, current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks. We propose a tea shoot instance segmentation model based on multi-scale mixed attention… More >

  • Open Access


    Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

    Chengjun Wang1,2, Fan Ding2,*, Yiwen Wang1, Renyuan Wu1, Xingyu Yao2, Chengjie Jiang1, Liuyi Ling1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876

    Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided context-feature pyramid network (AC-FPN) is… More >

  • Open Access


    An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

    Junpeng Wu1,2,*, Zhenpeng Liu2, Xingfan Jiang2, Xinguang Tao2, Ye Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 677-694, 2024, DOI:10.32604/cmc.2023.045759

    Abstract The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy… More >

  • Open Access


    Dynamic SLAM Visual Odometry Based on Instance Segmentation: A Comprehensive Review

    Jiansheng Peng1,2,*, Qing Yang1, Dunhua Chen1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 167-196, 2024, DOI:10.32604/cmc.2023.041900

    Abstract Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic… More >

  • Open Access


    FIR-YOLACT: Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

    Wen Dong1, Ziyan Liu1,2,*, Mo Yang1, Ying Wu1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3551-3572, 2023, DOI:10.32604/cmc.2023.044967

    Abstract Autonomous driving technology has made a lot of outstanding achievements with deep learning, and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems. The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information, which is more accurate and reliable than object detection. However, the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed. Therefore, this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT, which fuses the ICIoU (Improved Complete Intersection over Union) and Res2Net for the YOLACT algorithm. Specifically, the… More >

  • Open Access


    Improved Convolutional Neural Network for Traffic Scene Segmentation

    Fuliang Xu, Yong Luo, Chuanlong Sun, Hong Zhao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2691-2708, 2024, DOI:10.32604/cmes.2023.030940

    Abstract In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and short-term memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm’s capability to process both global and local information. Additionally, to increase the network’s initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset.… More >

  • Open Access


    High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

    Yong-Woon Kim1, Yung-Cheol Byun2,*, Dong Seog Han3, Dalia Dominic1, Sibu Cyriac1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4743-4762, 2022, DOI:10.32604/cmc.2022.028632

    Abstract A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes… More >

  • Open Access


    Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235

    Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds… More >

  • Open Access


    3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

    Siddiqui Muhammad Yasir1, Amin Muhammad Sadiq2, Hyunsik Ahn3,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5777-5791, 2022, DOI:10.32604/cmc.2022.025909

    Abstract 3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis. The computer vision, graphics, and machine learning fields have all given it a lot of attention. Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale data. Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision. However,… More >

  • Open Access


    A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

    Abdul Majid1, Sumaira Kausar1,*, Samabia Tehsin1, Amina Jameel2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 27-43, 2022, DOI:10.32604/csse.2022.022590

    Abstract In recent years, a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies. The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to. So it is required to combine segmentation and detection in a single framework. Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application. Scene understanding systems typically involves detection and segmentation of different natural and… More >

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