Classification System for Golf Ball Initial Conditions using CNN Based on High-Time Resolution Images Article Swipe
Tomomasa Yamasaki
,
Takashi Kaburagi
,
Toshiyuki Matsumoto
,
Satoshi Kumagai
,
Yosuke Kurihara
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2020.049
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2020.049
To accurately estimate the trajectory of a golf ball after a swing impact, the initial conditions—i.e., initial velocity, angle, and backspin—need to be considered. In this paper, we propose an estimation system that can classify these initial conditions of the golf ball by applying a convolutional neural network (CNN) to high-time resolution images captured by a line scan camera. In the proposed system, to characterize the combination of the three parameters on the high-time resolution images, a golf ball is marked with a black line around its great circle. Three CNNs are constructed for each initial condition, and each CNN is trained using simulation images. By applying high-timeresolution images, the three initial conditions unknown to the trained CNNs could be estimated. To evaluate the proposed system, we conducted a validity experiment using simulation images. The initial velocity was ranged from 10–80 m/s at 5 m/s intervals, the angle was ranged from 0–0.79 rad at 0.087 rad intervals, and the backspin was ranged from 100–450 rad/s at 50 rad/s intervals. The results were evaluated in terms of accuracy and the RMSE per range of each parameter ( RPR ): the initial velocity, angle, and backspin showed accuracies of 100.00%, 98.44%, and 73.13%, and RPR values of 0, 0.013, and 0.079, respectively
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.12792/iciae2020.049
- https://www2.ia-engineers.org/conference/index.php/iciae/iciae2020/paper/download/2332/1530
- OA Status
- gold
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3013293348
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- OpenAlex ID
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https://openalex.org/W3013293348Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.12792/iciae2020.049Digital Object Identifier
- Title
-
Classification System for Golf Ball Initial Conditions using CNN Based on High-Time Resolution ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Tomomasa Yamasaki, Takashi Kaburagi, Toshiyuki Matsumoto, Satoshi Kumagai, Yosuke KuriharaList of authors in order
- Landing page
-
https://doi.org/10.12792/iciae2020.049Publisher landing page
- PDF URL
-
https://www2.ia-engineers.org/conference/index.php/iciae/iciae2020/paper/download/2332/1530Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www2.ia-engineers.org/conference/index.php/iciae/iciae2020/paper/download/2332/1530Direct OA link when available
- Concepts
-
Convolutional neural network, Ball (mathematics), Swing, Artificial intelligence, Image resolution, Computer science, High resolution, Mean squared error, Line (geometry), Artificial neural network, Mathematics, Computer vision, Remote sensing, Geometry, Statistics, Geology, Physics, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CNNs | 90, 117 |
| abstract_inverted_index.RMSE | 179 |
| abstract_inverted_index.ball | 8, 41, 78 |
| abstract_inverted_index.each | 94, 98, 183 |
| abstract_inverted_index.from | 139, 150, 162 |
| abstract_inverted_index.golf | 7, 40, 77 |
| abstract_inverted_index.line | 56, 84 |
| abstract_inverted_index.scan | 57 |
| abstract_inverted_index.that | 32 |
| abstract_inverted_index.this | 25 |
| abstract_inverted_index.were | 171 |
| abstract_inverted_index.with | 81 |
| abstract_inverted_index.(CNN) | 48 |
| abstract_inverted_index.0.087 | 154 |
| abstract_inverted_index.Three | 89 |
| abstract_inverted_index.after | 9 |
| abstract_inverted_index.angle | 147 |
| abstract_inverted_index.black | 83 |
| abstract_inverted_index.could | 118 |
| abstract_inverted_index.great | 87 |
| abstract_inverted_index.rad/s | 164, 167 |
| abstract_inverted_index.range | 181 |
| abstract_inverted_index.swing | 11 |
| abstract_inverted_index.terms | 174 |
| abstract_inverted_index.these | 35 |
| abstract_inverted_index.three | 69, 110 |
| abstract_inverted_index.using | 102, 131 |
| abstract_inverted_index.0.013, | 206 |
| abstract_inverted_index.0.079, | 208 |
| abstract_inverted_index.angle, | 18, 191 |
| abstract_inverted_index.around | 85 |
| abstract_inverted_index.images | 52 |
| abstract_inverted_index.marked | 80 |
| abstract_inverted_index.neural | 46 |
| abstract_inverted_index.paper, | 26 |
| abstract_inverted_index.ranged | 138, 149, 161 |
| abstract_inverted_index.showed | 194 |
| abstract_inverted_index.system | 31 |
| abstract_inverted_index.values | 203 |
| abstract_inverted_index.10–80 | 140 |
| abstract_inverted_index.73.13%, | 200 |
| abstract_inverted_index.98.44%, | 198 |
| abstract_inverted_index.camera. | 58 |
| abstract_inverted_index.circle. | 88 |
| abstract_inverted_index.images, | 75, 108 |
| abstract_inverted_index.images. | 104, 133 |
| abstract_inverted_index.impact, | 12 |
| abstract_inverted_index.initial | 14, 16, 36, 95, 111, 135, 189 |
| abstract_inverted_index.network | 47 |
| abstract_inverted_index.propose | 28 |
| abstract_inverted_index.results | 170 |
| abstract_inverted_index.system, | 62, 125 |
| abstract_inverted_index.trained | 101, 116 |
| abstract_inverted_index.unknown | 113 |
| abstract_inverted_index.0–0.79 | 151 |
| abstract_inverted_index.100.00%, | 197 |
| abstract_inverted_index.accuracy | 176 |
| abstract_inverted_index.applying | 43, 106 |
| abstract_inverted_index.backspin | 159, 193 |
| abstract_inverted_index.captured | 53 |
| abstract_inverted_index.classify | 34 |
| abstract_inverted_index.estimate | 2 |
| abstract_inverted_index.evaluate | 122 |
| abstract_inverted_index.proposed | 61, 124 |
| abstract_inverted_index.validity | 129 |
| abstract_inverted_index.velocity | 136 |
| abstract_inverted_index.100–450 | 163 |
| abstract_inverted_index.conducted | 127 |
| abstract_inverted_index.evaluated | 172 |
| abstract_inverted_index.high-time | 50, 73 |
| abstract_inverted_index.parameter | 184 |
| abstract_inverted_index.velocity, | 17, 190 |
| abstract_inverted_index.accuracies | 195 |
| abstract_inverted_index.accurately | 1 |
| abstract_inverted_index.condition, | 96 |
| abstract_inverted_index.conditions | 37, 112 |
| abstract_inverted_index.estimated. | 120 |
| abstract_inverted_index.estimation | 30 |
| abstract_inverted_index.experiment | 130 |
| abstract_inverted_index.intervals, | 145, 156 |
| abstract_inverted_index.intervals. | 168 |
| abstract_inverted_index.parameters | 70 |
| abstract_inverted_index.resolution | 51, 74 |
| abstract_inverted_index.simulation | 103, 132 |
| abstract_inverted_index.trajectory | 4 |
| abstract_inverted_index.combination | 66 |
| abstract_inverted_index.considered. | 23 |
| abstract_inverted_index.constructed | 92 |
| abstract_inverted_index.characterize | 64 |
| abstract_inverted_index.respectively | 209 |
| abstract_inverted_index.convolutional | 45 |
| abstract_inverted_index.backspin—need | 20 |
| abstract_inverted_index.conditions—i.e., | 15 |
| abstract_inverted_index.high-timeresolution | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.02758647 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |