Object Detection Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 3009

Special Issue Editors

Department of Navigation and Observation, Naval Submarine Academy, Qingdao 266000, China
Interests: object detection; target recognition; synthetic aperture radar intepretation
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: computer vision; neural networks; object detection/classification/segmentation; remote sensing processing; synthetic aperture radar; millimeter wave radar technology
Special Issues, Collections and Topics in MDPI journals
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: interferometry synthetic aperture radar (InSAR); InSAR remote sensing; remote sensing processing; machine learning and deep learning; detection and classification using SAR images
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: artificial intelligence; multiobjective optimization; evolutionary and evolutionary multi-agent systems; mobile platforms

Special Issue Information

Dear Colleagues,

Object detection refers to the identification and tracking of important targets in several types of data and electromagnetic signals, such as visible and infrared spectrum, radar, sonar and synthetic aperture radar signals; acoustic and magnetic signals; and optical, spectral and medical data.

Today, it is a crucial basic technology and has many applications in industry and daily life. Within the past two decades—particularly since 2012, following the tremendous progress in sensor development and computer techniques such as deep learning—object detection entered a rapid development period, and remarkable theoretical achievements and practical applications have emerged.

While working on modern object detection techniques, researchers are faced with various types of sensors, data, requirements and applications. This Special Issue is considered a forum to present the progress and state-of-the-art of target detection technologies and their applications. Thus, we welcome research on new algorithms for object and target detection and tracking in different types of signals and data.

Dr. Jianwei Li
Dr. Tianwen Zhang
Prof. Dr. Xiaoling Zhang
Dr. Leszek Siwik
Guest Editors

Manuscript Submission Information

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Keywords

  • object detection
  • optical remote sensing
  • synthetic aperture radar object detection
  • magnetic target detection and localization
  • sonar object detection

Published Papers (3 papers)

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Research

17 pages, 2136 KiB  
Article
Adaptive Marginal Multi-Target Bayes Filter without Need for Clutter Density for Object Detection and Tracking
by Zongxiang Liu, Chunmei Zhou and Junwen Luo
Appl. Sci. 2023, 13(19), 11053; https://doi.org/10.3390/app131911053 - 07 Oct 2023
Viewed by 660
Abstract
The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of [...] Read more.
The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters. Full article
(This article belongs to the Special Issue Object Detection Technology)
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19 pages, 17944 KiB  
Article
Underwater Target Recognition via Cayley-Klein Measure and Shape Prior Information in Hyperspectral Imaging
by Bin Zhang, Fan Zhang, Yansen Sun, Xiaojie Li, Pei Liu, Liang Liu and Zelang Miao
Appl. Sci. 2023, 13(13), 7854; https://doi.org/10.3390/app13137854 - 04 Jul 2023
Viewed by 650
Abstract
Underwater target detection plays a vital role in various application scenarios, ranging from scientific research to military and industrial operations. In this paper, a detection method via the Cayley–Klein measure and a prior information of shape is proposed for the issue of hyperspectral [...] Read more.
Underwater target detection plays a vital role in various application scenarios, ranging from scientific research to military and industrial operations. In this paper, a detection method via the Cayley–Klein measure and a prior information of shape is proposed for the issue of hyperspectral underwater target identification. Firstly, by analyzing the data features of underwater targets and backgrounds, a background suppression algorithm based on Cayley–Klein measure is developed to enhance the differentiation between underwater targets and backgrounds. Then, a local peak-based algorithm is designed to discriminate potential underwater target points based on the local peak features of underwater targets. Finally, pseudo-target points are eliminated based on the priori shape information of underwater targets. Experiments show that the algorithm proposed is efficient and can effectively detect underwater targets from hyperspectral images. Full article
(This article belongs to the Special Issue Object Detection Technology)
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18 pages, 1726 KiB  
Article
Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
by Rui Ding, Zhuang Wang, Libing Jiang and Shuyu Zheng
Appl. Sci. 2023, 13(4), 2032; https://doi.org/10.3390/app13042032 - 04 Feb 2023
Cited by 1 | Viewed by 1146
Abstract
The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based [...] Read more.
The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments. Full article
(This article belongs to the Special Issue Object Detection Technology)
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