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Advances and Perspectives of Object Detection Using Sensing (and Processing)...
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Advances and Perspectives of Object Detection Using Sensing (and Processing) Method

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 4310

Special Issue Editors

Dr. Yi Chang

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: image enhancement; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Houzhang Fang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: object detection; deep learning
Prof. Dr. Luxin Yan

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: multispectral imaging processing; object detection

Special Issue Information

Dear Colleagues,

Object detection has been widely used in various applications, such as auto-driving, video surveillance, remote sensing, defect detecting, and so on. Most of the existing detection methods are designed for ideal circumstances, which would suffer a significant decrease in performance when used with degraded images. As for the outdoor environment, the imaging procedure is possibly contaminated by the sensor noise, rain/haze in adverse weather conditions, atmosphere turbulence, low light and so on. Therefore, it is highly desirable to explore the ways in which object detection can function under complex adverse conditions. This Special Issue aims to investigate the use of novel sensors and delicate algorithms to advance object detection under challenging adverse conditions. We would like to invite researchers to submit papers on the topic from all viewpoints, including theoretical issues, algorithms, systems, and industrial applications.

Dr. Yi Chang
Dr. Houzhang Fang
Prof. Dr. Luxin Yan
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. Sensors 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

  • image enhancement
  • computer vision
  • object detection
  • deep learning
  • multispectral imaging processing

Published Papers (3 papers)

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Research

16 pages, 18284 KiB  
Communication
A Self-Organizing Multi-Layer Agent Computing System for Behavioral Clustering Recognition
by Xingyu Qian, Aximu Yuemaier, Wenchi Yang, Xiaogang Chen, Longfei Liang, Shunfen Li, Weibang Dai and Zhitang Song
Sensors 2023, 23(12), 5435; https://doi.org/10.3390/s23125435 - 8 Jun 2023
Cited by 1 | Viewed by 736
Abstract
Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a [...] Read more.
Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a similarity comparison algorithm. Furthermore, in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes. Full article
(This article belongs to the Special Issue Advances and Perspectives of Object Detection Using Sensing (and Processing) Method)
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18 pages, 23972 KiB  
Article
Nonlinear Deblurring for Low-Light Saturated Image
by Shuning Cao, Yi Chang, Shengqi Xu, Houzhang Fang and Luxin Yan
Sensors 2023, 23(8), 3784; https://doi.org/10.3390/s23083784 - 7 Apr 2023
Viewed by 1382
Abstract
Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result [...] Read more.
Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images. To solve this problem, we formulate the saturation deblurring problem as a nonlinear model, in which all the saturated and unsaturated pixels are modeled adaptively. Specifically, we additionally introduce a nonlinear function to the convolution operator to accommodate the procedure of the saturation in the presence of the blurring. The proposed method has two advantages over previous methods. On the one hand, the proposed method achieves the same high quality of restoring the natural image as seen in conventional deblurring methods, while also reducing the estimation errors in saturated areas and suppressing ringing artifacts. On the other hand, compared with the recent saturated-based deblurring methods, the proposed method captures the formation of unsaturated and saturated degradations straightforwardly rather than with cumbersome and error-prone detection steps. Note that, this nonlinear degradation model can be naturally formulated into a maximum-a posterioriframework, and can be efficiently decoupled into several solvable sub-problems via the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real-world images demonstrate that the proposed deblurring algorithm outperforms the state-of-the-art low-light saturation-based deblurring methods. Full article
(This article belongs to the Special Issue Advances and Perspectives of Object Detection Using Sensing (and Processing) Method)
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18 pages, 4790 KiB  
Article
Visualization and Object Detection Based on Event Information
by Yinghong Fang, Yongjie Piao, Xiaoguang Xie, Miao Li, Xiaodong Li, Haolin Ji, Wei Xu and Tan Gao
Sensors 2023, 23(4), 1839; https://doi.org/10.3390/s23041839 - 7 Feb 2023
Viewed by 1310
Abstract
A dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a [...] Read more.
A dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a single event can only indicate that the increase or decrease in light exceeds the threshold at a certain pixel position and a certain moment. In order to further study the ability and characteristics of event information to represent targets, this paper proposes an event information visualization method with adaptive temporal resolution. Compared with methods with constant time intervals and a constant number of events, it can better convert event information into pseudo-frame images. Additionally, in order to explore whether the pseudo-frame image can efficiently complete the task of target detection according to its characteristics, this paper designs a target detection network named YOLOE. Compared with other algorithms, it has a more balanced detection effect. By constructing a dataset and conducting experimental verification, the detection accuracy of the image obtained by the event information visualization method with adaptive temporal resolution was 5.11% and 4.74% higher than that obtained using methods with a constant time interval and number of events, respectively. The average detection accuracy of pseudo-frame images in the YOLOE network designed in this paper is 85.11%, and the number of detection frames per second is 109. Therefore, the effectiveness of the proposed visualization method and the good performance of the designed detection network are verified. Full article
(This article belongs to the Special Issue Advances and Perspectives of Object Detection Using Sensing (and Processing) Method)
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