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Research on Deep Learning-based Object Detection and Its Derivative Key Technologies

Submission Deadline: 31 March 2025 View: 1411 Submit to Special Issue

Guest Editors

Dr. Guanqiu Qi, State University of New York at Buffalo State, USA

Dr. Zhiqin Zhu, Chongqing University of Posts and Telecommunications, China

Dr. Zhihao Zhou, Chongqing University of Posts and Telecommunications, China

Prof. Yinong Chen, Arizona State University, USA

Summary

In digital image processing, the detection of specific objects and the resulting tasks of object classification, recognition, and segmentation are extremely important. These related technologies have now been widely applied in various fields such as autonomous driving, aerospace, medical diagnosis, remote sensing image analysis, and more. They have achieved numerous breakthrough applications, transforming the path of human societal progress. In recent years, with the rapid evolution of deep learning technology, object detection-related technologies have further developed at a fast pace. They have been extensively applied to the identification and detection of various signs and objects in autonomous driving, autonomous flight and delivery by drones, tumor segmentation and lesion diagnosis in medical imaging, and the interpretation and key object recognition in remote sensing imagery, all of which have advanced the mode of social operation.


Keywords

Project topics include, but are not limited to, the following:
Image Object Detection
Object Detection for Autopilot
Image Segmentation for Autopilot
Object Detection for Remote Sensing
Image Segmentation for Remote Sensing
Medical Image lesion Detection
Medical Image Segmentation
Image Object Classification
Object Classification for Autopilot
Human Machine Interface
Flexible sensing technology
Flexible display
Data security and privacy considerations in digital health solutions

Published Papers


  • Open Access

    ARTICLE

    EGSNet: An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

    Guojun Chen, Tao Cui, Yongjie Hou, Huihui Li
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056093
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance… More >

  • Open Access

    ARTICLE

    An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

    Keke Zhou, Guoqiang Zheng, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang
    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >

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