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Article: Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation
Title | Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation |
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Authors | |
Keywords | Clustering methods machine learning algorithms machine vision object detection |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 |
Citation | IEEE Access, 2020, v. 8, p. 193512-193522 How to Cite? |
Abstract | Visual perception is critical and essential to understand phenomenon and environments of the world. Pervasively configured devices like cameras are key in dynamic status monitoring, object detection and recognition. As such, visual sensor environments using one single or multiple cameras must deal with a huge amount of high-resolution images, videos or other multimedia. In this paper, to promote smart advancement and fast detection of visual environments, we propose a deep transfer learning strategy for real-time target detection for situations where acquiring large-scale data is complicated and challenging. By employing the concept of transfer learning and pre-training the network with established datasets, apart from the outstanding performance in target localization and recognition can be achieved, time consumption of training a deep model is also significantly reduced. Besides, the original clustering method, k-means, in the You Only Look Once (YOLOv3) detection model is sensitive to the initial cluster centers when estimating the initial width and height of the predicted bounding boxes, thereby processing large-scale data is extremely time-consuming. To handle such problems, an improved clustering method, mini batch k-means++ is incorporated into the detection model to improve the clustering accuracy. We examine the sustainable outperformance in three typical applications, digital pathology, smart agriculture and remote sensing, in vision-based sensing environments. |
Persistent Identifier | http://hdl.handle.net/10722/303941 |
ISSN | 2021 Impact Factor: 3.476 2020 SCImago Journal Rankings: 0.587 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ren, Z | - |
dc.contributor.author | Lam, EY | - |
dc.contributor.author | Zhao, J | - |
dc.date.accessioned | 2021-09-23T08:52:57Z | - |
dc.date.available | 2021-09-23T08:52:57Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Access, 2020, v. 8, p. 193512-193522 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303941 | - |
dc.description.abstract | Visual perception is critical and essential to understand phenomenon and environments of the world. Pervasively configured devices like cameras are key in dynamic status monitoring, object detection and recognition. As such, visual sensor environments using one single or multiple cameras must deal with a huge amount of high-resolution images, videos or other multimedia. In this paper, to promote smart advancement and fast detection of visual environments, we propose a deep transfer learning strategy for real-time target detection for situations where acquiring large-scale data is complicated and challenging. By employing the concept of transfer learning and pre-training the network with established datasets, apart from the outstanding performance in target localization and recognition can be achieved, time consumption of training a deep model is also significantly reduced. Besides, the original clustering method, k-means, in the You Only Look Once (YOLOv3) detection model is sensitive to the initial cluster centers when estimating the initial width and height of the predicted bounding boxes, thereby processing large-scale data is extremely time-consuming. To handle such problems, an improved clustering method, mini batch k-means++ is incorporated into the detection model to improve the clustering accuracy. We examine the sustainable outperformance in three typical applications, digital pathology, smart agriculture and remote sensing, in vision-based sensing environments. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers: Open Access Journals. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639 | - |
dc.relation.ispartof | IEEE Access | - |
dc.rights | IEEE Access. Copyright © Institute of Electrical and Electronics Engineers: Open Access Journals. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Clustering methods | - |
dc.subject | machine learning algorithms | - |
dc.subject | machine vision | - |
dc.subject | object detection | - |
dc.title | Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation | - |
dc.type | Article | - |
dc.identifier.email | Lam, EY: elam@eee.hku.hk | - |
dc.identifier.authority | Lam, EY=rp00131 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3032955 | - |
dc.identifier.scopus | eid_2-s2.0-85096036661 | - |
dc.identifier.hkuros | 324987 | - |
dc.identifier.volume | 8 | - |
dc.identifier.spage | 193512 | - |
dc.identifier.epage | 193522 | - |
dc.identifier.isi | WOS:000587837400001 | - |
dc.publisher.place | United States | - |