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Mathematics
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Object Detection: Algorithms, Computations and Practices
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Object Detection: Algorithms, Computations and Practices

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10160

Special Issue Editors

Dr. Siyu Xia

E-Mail Website
Guest Editor
School of Automation, Southeast University, Nanjing 210096, China
Interests: image processing; applied machine learning; image generation; intelligent vision systems
Dr. Libo Sun

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: virtual reality; augmented reality; computer animation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your work to our journal. Object detection is the application of computer vision, pattern recognition and image processing and other technologies to detect instances of objects of a certain class within an image, and its application scenarios cover important areas such as intelligent security, traffic surveillance, scene understanding, and autonomous driving. In recent years, the rapid development of deep learning techniques has brought tremendous advances in the field of object detection. However, there are still some practical problems that have not yet been solved, such as the occlusions of object in pedestrian detection, scene text detection and recognition, few-shot object detection, tiny object detection, interpretable learning, face detection and recognition in complex real scenes, model drift and re-detection problems in object tracking, etc. Therefore, much efforts have to be engaged to remarkably improve the performance of object detection.

This Special Issue aims to discuss and solve the challenging problems related to object detection within the framework of deep learning. We invite authors to submit manuscripts that are highly related to the topics of this special issue and which have not been published before. The topics of interest include, but are not limited to:

  • Anchor and Anchor-free object detection;
  • Few-shot/zero-shot object detection;
  • Weak/semi/unsupervised object detection;
  • Long-tailed object detection;
  • Small object detection;
  • 3D object detection;
  • Object detection in challenging conditions;
  • Fusion of point cloud and images for object detection;
  • Large-scale datasets for object detection.

Dr. Siyu Xia
Dr. Libo Sun
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. Mathematics 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 2600 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

  • mathematics and knowledge graph
  • knowledge graph construction
  • knowledge graph alignment
  • knowledge graph reasoning
  • knowledge graph querying
  • knowledge graph embedding
  • multimodal knowledge graph
  • multilingual knowledge graph
  • nlp and knowledge graph
  • data mining and knowledge graph
  • machine learning on graphs
  • question answering on knowledge graph
  • semantic search
  • ontology engineering
  • linked open data
  • knowledge graph applications in medicine, law, security, and smart grid

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

11 pages, 9641 KiB  
Article
Automatic 3D Modeling Technique for Transmission Towers from 2D Drawings
by Ziqiang Tang, Chao Han, Hongwu Li, Zhou Fan, Ke Sun, Yuntian Huang, Yuxin Chen and Chenxing Wang
Mathematics 2024, 12(23), 3767; https://doi.org/10.3390/math12233767 - 29 Nov 2024
Viewed by 747
Abstract
The 3D modeling of transmission towers currently depends on manual operations, resulting in high labor and time costs. To this end, an automatic 3D modeling technique based on 2D drawings is proposed. Using this method, the 2D drawings of transmission towers were analyzed [...] Read more.
The 3D modeling of transmission towers currently depends on manual operations, resulting in high labor and time costs. To this end, an automatic 3D modeling technique based on 2D drawings is proposed. Using this method, the 2D drawings of transmission towers were analyzed first, then a 3D model of a tower was reconstructed using a counter-to-detail strategy. The analysis of the 2D drawings aimed to segment the geometric shapes and subsequently extract the vectors. All obtained vectors were classified into outer contour vectors and internal structure vectors. For each tower section, the 3D outer contour framework was constructed first using the wireframe model algorithm, followed by the assembly of internal details onto the 3D contour framework to fully reconstruct the 3D model. Experiments demonstrated that constructed 3D models exhibited high accuracy, with an average chamfer distance to the real scanned dense LiDAR point clouds of less than 0.05 m, which was less than 1% relative to the whole size of the created models. Furthermore, the automation of this technique implies its potential for various applications. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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31 pages, 1871 KiB  
Article
3D Reconstruction of Geometries for Urban Areas Supported by Computer Vision or Procedural Generations
by Hanli Liu, Carlos J. Hellín, Abdelhamid Tayebi, Carlos Delgado and Josefa Gómez
Mathematics 2024, 12(21), 3331; https://doi.org/10.3390/math12213331 - 23 Oct 2024
Cited by 1 | Viewed by 1181
Abstract
This work presents a numerical mesh generation method for 3D urban scenes that could be easily converted into any 3D format, different from most implementations which are limited to specific environments in their applicability. The building models have shaped roofs and faces with [...] Read more.
This work presents a numerical mesh generation method for 3D urban scenes that could be easily converted into any 3D format, different from most implementations which are limited to specific environments in their applicability. The building models have shaped roofs and faces with static colors, combining the buildings with a ground grid. The building generation uses geographic positions and shape names, which can be extracted from OpenStreetMap. Additional steps, like a computer vision method, can be integrated into the generation optionally to improve the quality of the model, although this is highly time-consuming. Its function is to classify unknown roof shapes from satellite images with adequate resolution. The generation can also use custom geographic information. This aspect was tested using information created by procedural processes. The method was validated by results generated for many realistic scenarios with multiple building entities, comparing the results between using computer vision and not. The generated models were attempted to be rendered under Graphics Library Transmission Format and Unity Engine. In future work, a polygon-covering algorithm needs to be completed to process the building footprints more effectively, and a solution is required for the missing height values in OpenStreetMap. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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13 pages, 833 KiB  
Article
Multi-Layer Feature Restoration and Projection Model for Unsupervised Anomaly Detection
by Fuzhen Cai and Siyu Xia
Mathematics 2024, 12(16), 2480; https://doi.org/10.3390/math12162480 - 11 Aug 2024
Cited by 1 | Viewed by 1029
Abstract
The anomaly detection of products is a classical problem in the field of computer vision. Image reconstruction-based methods have shown promising results in the field of abnormality detection. Most of the existing methods use convolutional neural networks to build encoding–decoding structures to do [...] Read more.
The anomaly detection of products is a classical problem in the field of computer vision. Image reconstruction-based methods have shown promising results in the field of abnormality detection. Most of the existing methods use convolutional neural networks to build encoding–decoding structures to do image restoration. However, the limited receptive field of convolutional neural networks makes the information considered in the image restoration process limited, and the downsampling in the encoder causes information loss, which is not conducive to performing fine-grained restoration of images. To solve this problem, we propose a multi-layer feature restoration and projection model (MLFRP), which enables the restoration process to be carried out on multi-scale feature maps through a block-level feature restoration module that fully considers the detail information and semantic information required for the restoration process. We conducted in-depth experiments on the MvtecAD anomaly detection benchmark dataset, which showed that our model outperforms current state-of-the-art anomaly detection methods. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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14 pages, 11397 KiB  
Article
Improving the Performance of Object Detection by Preserving Balanced Class Distribution
by Heewon Lee and Sangtae Ahn
Mathematics 2023, 11(21), 4460; https://doi.org/10.3390/math11214460 - 27 Oct 2023
Cited by 1 | Viewed by 2684
Abstract
Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized [...] Read more.
Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class, is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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14 pages, 5734 KiB  
Article
Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects
by Seong-Hwan Kang, Vikas Palakonda, Il-Min Kim, Jae-Mo Kang and Sangseok Yun
Mathematics 2023, 11(18), 3898; https://doi.org/10.3390/math11183898 - 13 Sep 2023
Cited by 5 | Viewed by 2430
Abstract
Quality control in manufacturing equipment relies heavily on the detection of steel surface defects. Recently, there have been an increasing number of efforts in which object detection techniques have been utilized to achieve promising results in the detection of steel surface defects since [...] Read more.
Quality control in manufacturing equipment relies heavily on the detection of steel surface defects. Recently, there have been an increasing number of efforts in which object detection techniques have been utilized to achieve promising results in the detection of steel surface defects since the defect patterns can be considered objects. To enhance the detection performance in the object detection problem, the non-maximum suppression (NMS) step, which eliminates redundant boxes overlapped with a box having the greatest detection score, is essential. In this work, we propose a novel NMS to improve the detection method of steel surface defects. The proposed NMS approach is composed of three novel techniques: IoU regularization, threshold adjustment, and comparison rule modification to enhance the detection performance. To evaluate the performance of the proposed NMS, we carry out extensive numerical experiments using the YOLOv7 and EfficientDet models on the steel surface defect datasets, NEU-DET and GC10-DET. The experimental results demonstrate that the proposed NMS outperforms the conventional NMS methods in both quantitative and qualitative manners. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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13 pages, 2112 KiB  
Article
Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition
by Xiaohe Li and Wankou Yang
Mathematics 2023, 11(12), 2667; https://doi.org/10.3390/math11122667 - 12 Jun 2023
Cited by 1 | Viewed by 1098
Abstract
Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries [...] Read more.
Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images. Full article
(This article belongs to the Special Issue Object Detection: Algorithms, Computations and Practices)
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