Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm
This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by...
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| Veröffentlicht in: | Security and communication networks 2021, Vol.2021, p.1-10 |
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| creator | Jiang, Hangsheng |
| description | This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability. |
| doi_str_mv | 10.1155/2021/9996736 |
| format | Article |
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The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2021/9996736</identifier><language>eng</language><publisher>London: Hindawi</publisher><subject>Adaptive algorithms ; Adaptive filters ; Algorithms ; Artificial neural networks ; Basketball ; Cameras ; Convolution ; Feature extraction ; Formability ; Image acquisition ; Image processing ; Infrared imagery ; Noise ; Noise reduction ; Object recognition ; Wavelet analysis</subject><ispartof>Security and communication networks, 2021, Vol.2021, p.1-10</ispartof><rights>Copyright © 2021 Hangsheng Jiang.</rights><rights>Copyright © 2021 Hangsheng Jiang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-895fdec44b56d0549d3b5539b4d371b2c32cd812416f28cef12f23852e969c153</citedby><cites>FETCH-LOGICAL-c337t-895fdec44b56d0549d3b5539b4d371b2c32cd812416f28cef12f23852e969c153</cites><orcidid>0000-0002-4171-3010</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4009,27902,27903,27904</link.rule.ids></links><search><contributor>Chen, Chi-Hua</contributor><contributor>Chi-Hua Chen</contributor><creatorcontrib>Jiang, Hangsheng</creatorcontrib><title>Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm</title><title>Security and communication networks</title><description>This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.</description><subject>Adaptive algorithms</subject><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Basketball</subject><subject>Cameras</subject><subject>Convolution</subject><subject>Feature extraction</subject><subject>Formability</subject><subject>Image acquisition</subject><subject>Image processing</subject><subject>Infrared imagery</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Object recognition</subject><subject>Wavelet 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Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm</atitle><jtitle>Security and communication networks</jtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1939-0114</issn><eissn>1939-0122</eissn><abstract>This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.</abstract><cop>London</cop><pub>Hindawi</pub><doi>10.1155/2021/9996736</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-4171-3010</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Adaptive algorithms Adaptive filters Algorithms Artificial neural networks Basketball Cameras Convolution Feature extraction Formability Image acquisition Image processing Infrared imagery Noise Noise reduction Object recognition Wavelet analysis |
| title | Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm |
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