Review of Deep Convolution Applied to Target Detection Algorithms
As one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent year...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-05-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/1652926737069-43486494.pdf |
| _version_ | 1811231611793965056 |
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| author | DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu |
| author_facet | DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu |
| author_sort | DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu |
| collection | DOAJ |
| description | As one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent years, with the breakthroughs in computer hardware resources and depth convolution algorithms in image classification tasks, depth convolution-based target detection algorithms have gradually replaced the traditional target detection algorithms and achieved significant results in terms of accuracy and performance. This paper reviews the current research status of depth convolution-based target detection algorithms and possible future development directions. It introduces the authoritative datasets and evaluation metrics of target detection algorithms with the limitations of traditional target detection algorithms as a guide, and then reviews the research and development history of representative algorithms for depth convolution-based target detection in recent years with time and algorithm architecture as the main research lines. The network structures of one-stage, two-stage and other improved algorithms are compared and analyzed, and the characteristics, advantages and limitations of various target detection algorithms are summarized. Finally, the future trends are prospected in the light of current problems and challenges of target detection. |
| first_indexed | 2024-04-12T10:47:55Z |
| format | Article |
| id | doaj.art-8ad357676eea4825998a528b89168510 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-12T10:47:55Z |
| publishDate | 2022-05-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-8ad357676eea4825998a528b891685102022-12-22T03:36:20ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-05-011651025104210.3778/j.issn.1673-9418.2111063Review of Deep Convolution Applied to Target Detection AlgorithmsDONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu0School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong 266061, ChinaAs one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent years, with the breakthroughs in computer hardware resources and depth convolution algorithms in image classification tasks, depth convolution-based target detection algorithms have gradually replaced the traditional target detection algorithms and achieved significant results in terms of accuracy and performance. This paper reviews the current research status of depth convolution-based target detection algorithms and possible future development directions. It introduces the authoritative datasets and evaluation metrics of target detection algorithms with the limitations of traditional target detection algorithms as a guide, and then reviews the research and development history of representative algorithms for depth convolution-based target detection in recent years with time and algorithm architecture as the main research lines. The network structures of one-stage, two-stage and other improved algorithms are compared and analyzed, and the characteristics, advantages and limitations of various target detection algorithms are summarized. Finally, the future trends are prospected in the light of current problems and challenges of target detection.http://fcst.ceaj.org/fileup/1673-9418/PDF/1652926737069-43486494.pdf|computer vision|deep convolution|target detection|one-stage|two-stage |
| spellingShingle | DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu Review of Deep Convolution Applied to Target Detection Algorithms Jisuanji kexue yu tansuo |computer vision|deep convolution|target detection|one-stage|two-stage |
| title | Review of Deep Convolution Applied to Target Detection Algorithms |
| title_full | Review of Deep Convolution Applied to Target Detection Algorithms |
| title_fullStr | Review of Deep Convolution Applied to Target Detection Algorithms |
| title_full_unstemmed | Review of Deep Convolution Applied to Target Detection Algorithms |
| title_short | Review of Deep Convolution Applied to Target Detection Algorithms |
| title_sort | review of deep convolution applied to target detection algorithms |
| topic | |computer vision|deep convolution|target detection|one-stage|two-stage |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/1652926737069-43486494.pdf |
| work_keys_str_mv | AT dongwenxuanlianghongtaoliuguozhuhuqiangyuxu reviewofdeepconvolutionappliedtotargetdetectionalgorithms |