Complexity and accuracy analysis of common artificial neural networks on pedestrian detection
With the development of computer version, deep learning and artificial neural networks approaches like SPP-net, Faster-RCNN and YOLO are proposed. This paper compares them in terms of efficiency and effectiveness. By analyzing the network architecture, SPP-net is more complex than Faster-RCNN and YO...
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EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201823201003 |
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doaj-0f5140b246024cdfaf1b7ed5e193d64e2021-02-02T08:26:41ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012320100310.1051/matecconf/201823201003matecconf_eitce2018_01003Complexity and accuracy analysis of common artificial neural networks on pedestrian detectionWu Jiatu0School of Data and Computer Science, Sun Yat-sen University (SYSU)With the development of computer version, deep learning and artificial neural networks approaches like SPP-net, Faster-RCNN and YOLO are proposed. This paper compares them in terms of efficiency and effectiveness. By analyzing the network architecture, SPP-net is more complex than Faster-RCNN and YOLO. By analyzing the experiments, SPP-net and Faster-RCNN are more accurate than YOLO in static detection while opposite in real-time system. Therefore, in real-time pedestrian detection situation, YOLO can perform better. In static pedestrian detection situation, Faster-RCNN or SPP-net can perform better.https://doi.org/10.1051/matecconf/201823201003 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Wu Jiatu |
| spellingShingle |
Wu Jiatu Complexity and accuracy analysis of common artificial neural networks on pedestrian detection MATEC Web of Conferences |
| author_facet |
Wu Jiatu |
| author_sort |
Wu Jiatu |
| title |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| title_short |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| title_full |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| title_fullStr |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| title_full_unstemmed |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| title_sort |
complexity and accuracy analysis of common artificial neural networks on pedestrian detection |
| publisher |
EDP Sciences |
| series |
MATEC Web of Conferences |
| issn |
2261-236X |
| publishDate |
2018-01-01 |
| description |
With the development of computer version, deep learning and artificial neural networks approaches like SPP-net, Faster-RCNN and YOLO are proposed. This paper compares them in terms of efficiency and effectiveness. By analyzing the network architecture, SPP-net is more complex than Faster-RCNN and YOLO. By analyzing the experiments, SPP-net and Faster-RCNN are more accurate than YOLO in static detection while opposite in real-time system. Therefore, in real-time pedestrian detection situation, YOLO can perform better. In static pedestrian detection situation, Faster-RCNN or SPP-net can perform better. |
| url |
https://doi.org/10.1051/matecconf/201823201003 |
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AT wujiatu complexityandaccuracyanalysisofcommonartificialneuralnetworksonpedestriandetection |
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