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

Submission Deadline: 31 March 2025 (closed) View: 3279 Submit to Journal

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

    Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network for Infrared Small Target Detection

    Siqi Zhang, Shengda Pan
    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4655-4676, 2025, DOI:10.32604/cmc.2025.064864
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,… More >

  • Open Access

    ARTICLE

    Zero-Shot Based Spatial AI Algorithm for Up-to-Date 3D Vision Map Generations in Highly Complex Indoor Environments

    Sehun Lee, Taehoon Kim, Junho Ahn
    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3623-3648, 2025, DOI:10.32604/cmc.2025.063985
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multi-dimensional vision identification technology adapted to the situation in large indoor and underground spaces. With the expansion of large shopping malls and underground urban spaces (UUS), there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation, remodeling, and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps. The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site… More >

  • Open Access

    ARTICLE

    Implicit Feature Contrastive Learning for Few-Shot Object Detection

    Gang Li, Zheng Zhou, Yang Zhang, Chuanyun Xu, Zihan Ruan, Pengfei Lv, Ru Wang, Xinyu Fan, Wei Tan
    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1615-1632, 2025, DOI:10.32604/cmc.2025.063109
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Although conventional object detection methods achieve high accuracy through extensively annotated datasets, acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications. Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples. However, the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution, which consequently impacts model performance. Inspired by contrastive learning principles, we propose an Implicit Feature Contrastive Learning (IFCL) module to address this limitation and augment feature diversity More >

  • Open Access

    ARTICLE

    Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task

    Yang Su, Xianrang Shi, Tinglun Song
    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

  • Open Access

    ARTICLE

    YOLO-AB: A Fusion Algorithm for the Elders’ Falling and Smoking Behavior Detection Based on Improved YOLOv8

    Xianghong Cao, Chenxu Li, Haoting Zhai
    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5487-5515, 2025, DOI:10.32604/cmc.2025.061823
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract The behavior safety testing of more and more elderly people living alone has become a hot research topic along with the arrival of an aging society. A YOLO-Abnormal Behaviour (YOLO-AB) algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper, which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly, to solve the problems of single detection type, low fusion detection accuracy, and high missed detection rate. Firstly, datasets… More >

  • Open Access

    ARTICLE

    Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells

    Chuanyun Xu, Die Hu, Yang Zhang, Shuaiye Huang, Yisha Sun, Gang Li
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 559-574, 2025, DOI:10.32604/cmc.2025.061579
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer. However, this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size. Pathologists often refer to surrounding cells to identify abnormalities. To emulate this slide examination behavior, this study proposes a Multi-Scale Feature Fusion Network (MSFF-Net) for detecting cervical abnormal cells. MSFF-Net employs a Cross-Scale Pooling Model (CSPM) to effectively capture diverse features and contextual information, ranging from local details to the overall structure. Additionally, a Multi-Scale Fusion Attention (MSFA)… More >

  • Open Access

    ARTICLE

    YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

    Honglin Wang, Yangyang Zhang, Cheng Zhu
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3399-3417, 2025, DOI:10.32604/cmc.2024.058932
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >

  • Open Access

    ARTICLE

    Side-Scan Sonar Image Detection of Shipwrecks Based on CSC-YOLO Algorithm

    Shengxi Jiao, Fenghao Xu, Haitao Guo
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3019-3044, 2025, DOI:10.32604/cmc.2024.057192
    (This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
    Abstract Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of object detection in shipwreck side-scan sonar images, due to the complex seabed environment, it is difficult to extract object features, often leading to missed detections of shipwreck images and slow detection speed. To address these issues, this paper proposes an object detection algorithm, CSC-YOLO (Context Guided Block, Shared Conv_Group Normalization Detection, Cross Stage Partial with 2 Partial Convolution-You Only Look… More >

  • 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, Vol.81, No.3, pp. 3969-3987, 2024, 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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