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New Insights in 2D and 3D Object Detection and Semantic...
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New Insights in 2D and 3D Object Detection and Semantic Segmentation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 2437

Special Issue Editors

Dr. Chang Liu

E-Mail Website
Guest Editor
Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA
Interests: remote sensing; image processing; lidar data analysis; AI; deep learning; computer vision
Dr. Sara Shirowzhan

E-Mail Website
Guest Editor
City Futures Research Centre, School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the fast development of Industry 4.0, the detection and semantic segmentation of two-dimensional (2D) images and three-dimensional (3D) objects have been applied in numerous fields, such as medical imaging, remote sensing, autonomous vehicles, industrial automation, urban studies, robotics, and virtual reality. This Special Issue focuses on advancing techniques and methodologies (such as AI) in the fields of image processing, 3D object detection and semantic segmentation. The collection underscores the interdisciplinary nature of research in several disciplines, highlighting how these innovations contribute to transformative applications across diverse fields. Each contribution represents a significant step forward in advancing technology, paving the way for new capabilities and applications in both academic research and industrial applications. Potential topics include, but are not limited to, the following:

  • AI and image segmentation;
  • AI and 3D segmentation;
  • Object detection;
  • Semantic segmentation and scene understanding;
  • Remote sensing, virtual reality, robotics, medical, biomedical 2D and 3D imaging;
  • Lidar, depth cameras, and RGB-D sensors;
  • Data processing for smart cities;
  • AI methods applied in images: object detection, pose detection, object tracking, semantic segmentation;
  • Deep learning approaches for 2D or 3D object detection and segmentation;
  • Interactive and semi-supervised approaches;
  • Domain adaptation and transfer learning.

Dr. Chang Liu
Dr. Sara Shirowzhan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data segmentation
  • artificial intelligence (AI)
  • augmented reality
  • autonomous navigation
  • autonomous vehicles
  • biomedical imaging
  • computer vision
  • convolutional neural networks (CNNs)
  • deep learning
  • depth camera
  • GIS
  • image segmentation
  • industrial automation
  • lidar
  • medical data
  • object detection
  • remote sensing
  • robotics
  • segmentation
  • semantic segmentation
  • smart cities
  • transfer learning
  • virtual reality

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Published Papers (2 papers)

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24 pages, 7683 KiB  
Article
Hybrid-DETR: A Differentiated Module-Based Model for Object Detection in Remote Sensing Images
by Mingji Yang, Rongyu Xu, Chunyu Yang, Haibin Wu and Aili Wang
Electronics 2024, 13(24), 5014; https://doi.org/10.3390/electronics13245014 - 20 Dec 2024
Viewed by 984
Abstract
Currently, embedded unmanned aerial vehicle (UAV) systems face significant challenges in balancing detection accuracy and computational efficiency when processing remote sensing images with complex backgrounds, small objects, and occlusions. This paper proposes the Hybrid-DETR model based on a real-time end-to-end Detection Transformer (RT-DETR), [...] Read more.
Currently, embedded unmanned aerial vehicle (UAV) systems face significant challenges in balancing detection accuracy and computational efficiency when processing remote sensing images with complex backgrounds, small objects, and occlusions. This paper proposes the Hybrid-DETR model based on a real-time end-to-end Detection Transformer (RT-DETR), featuring a novel HybridNet backbone network that implements a differentiated hybrid structure through lightweight RepConv Cross-stage Partial Efficient Layer Aggregation Network (RCSPELAN) modules and the Heat-Transfer Cross-stage Fusion (HTCF) modules, effectively balancing feature extraction efficiency and global perception capabilities. Additionally, we introduce a Small-Object Detection Module (SODM) and an EIFI module to enhance the detection capability of small objects in complex scenarios, while employing the Focaler-Shape-IoU loss function to optimize bounding box regression. Experimental results on the VisDrone2019 dataset demonstrate that Hybrid-DETR achieves mAP50 and mAP50:95 scores of 52.2% and 33.3%, respectively, representing improvements of 5.2% and 4.3% compared to RT-DETR-R18, while reducing model parameters by 29.33%. The effectiveness and robustness of our improved method are further validated on multiple challenging datasets, including AI-TOD and HIT-UAV. Full article
(This article belongs to the Special Issue New Insights in 2D and 3D Object Detection and Semantic Segmentation)
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19 pages, 6837 KiB  
Article
A Classification and Segmentation Model for Diamond Abrasive Grains Based on Improved Swin-Unet-SAM
by Yanfen Lin, Tinghao Fan and Congfu Fang
Electronics 2024, 13(21), 4213; https://doi.org/10.3390/electronics13214213 - 27 Oct 2024
Viewed by 931
Abstract
The detection of abrasive grain images in diamond tools serves as the foundation for assessing the overall condition of the tools, encompassing crucial aspects of diamond abrasive grains like the quantity, size, morphology, and distribution. Given the intricate background textures and reflective characteristics [...] Read more.
The detection of abrasive grain images in diamond tools serves as the foundation for assessing the overall condition of the tools, encompassing crucial aspects of diamond abrasive grains like the quantity, size, morphology, and distribution. Given the intricate background textures and reflective characteristics exhibited by diamond images, diamond detection and segmentation pose a significant challenge. Recently, numerous defect detection methods based on machine learning and deep learning have emerged. However, several issues persist, such as detection accuracy and the interference caused by intricate background textures. The present work demonstrates an efficient classification and segmentation network algorithm that combines Swin-Unet with SAM (Segment Anything Model) to alleviate the existing problems. Specifically, four embedding structures were devised to bridge the two models for iterative training. The transformer blocks within the Swin-Unet model were enhanced to facilitate classification and coarse segmentation, and the mask structure in SAM was refined to enable fine segmentation. The experimental results show that under a small sample dataset with complex background textures, the average index values of ACC (accuracy), SE (Sensitivity), and DSC (Dice Similarity Coefficient) for the classification and segmentation of diamond abrasive grains reached 98.7%, 92.5%, and 85.9%, respectively. Compared with the model before improvement, its ACC, SE and DSC increased by 1.2%, 15.9%, and 7.6%, respectively. The test results, based on four different datasets, consistently indicated that this model has excellent segmentation performance and robustness and has great application potential in the industrial field. Full article
(This article belongs to the Special Issue New Insights in 2D and 3D Object Detection and Semantic Segmentation)
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