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A Lean Convolutional Neural Network for Vehicle Classification. / Sanchez-Castro, Jonathan J.; Rodriguez-Quinonez, Julio C.; Ramirez-Hernandez, Luis R. et al.
2020 IEEE 29th International Symposium on Industrial Electronics (ISIE): 17 - 19 June, 2020, Delft, Netherlands, Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. p. 1365-1369 9152274 (IEEE International Symposium on Industrial Electronics (ISIE); Vol. 2020).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Sanchez-Castro, JJ, Rodriguez-Quinonez, JC, Ramirez-Hernandez, LR, Galaviz, G, Hernandez-Balbuena, D, Trujillo-Hernandez, G, Flores-Fuentes, W
, Mercorelli, P, Hernandez-Perdomo, W, Sergiyenko, O & Gonzalez-Navarro, FF 2020,
A Lean Convolutional Neural Network for Vehicle Classification. in
2020 IEEE 29th International Symposium on Industrial Electronics (ISIE): 17 - 19 June, 2020, Delft, Netherlands, Proceedings., 9152274, IEEE International Symposium on Industrial Electronics (ISIE), vol. 2020, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 1365-1369, 29th IEEE International Symposium on Industrial Electronics, ISIE 2020, Delft, Netherlands,
17.06.20.
https://doi.org/10.1109/ISIE45063.2020.9152274APA
Sanchez-Castro, J. J., Rodriguez-Quinonez, J. C., Ramirez-Hernandez, L. R., Galaviz, G., Hernandez-Balbuena, D., Trujillo-Hernandez, G., Flores-Fuentes, W.
, Mercorelli, P., Hernandez-Perdomo, W., Sergiyenko, O., & Gonzalez-Navarro, F. F. (2020).
A Lean Convolutional Neural Network for Vehicle Classification. In
2020 IEEE 29th International Symposium on Industrial Electronics (ISIE): 17 - 19 June, 2020, Delft, Netherlands, Proceedings (pp. 1365-1369). Article 9152274 (IEEE International Symposium on Industrial Electronics (ISIE); Vol. 2020). IEEE - Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/ISIE45063.2020.9152274Vancouver
Sanchez-Castro JJ, Rodriguez-Quinonez JC, Ramirez-Hernandez LR, Galaviz G, Hernandez-Balbuena D, Trujillo-Hernandez G et al.
A Lean Convolutional Neural Network for Vehicle Classification. In 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE): 17 - 19 June, 2020, Delft, Netherlands, Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2020. p. 1365-1369. 9152274. (IEEE International Symposium on Industrial Electronics (ISIE)). doi: 10.1109/ISIE45063.2020.9152274
Bibtex
@inbook{6d2be7dcc7cc444a89d7d9ea508867a2,
title = "A Lean Convolutional Neural Network for Vehicle Classification",
abstract = "Image classification is an important task in machine vision, in which vehicle classification is used for different applications like traffic analysis, autonomous driving, security, among others. Recent studies made with Convolutional Neural Networks (CNN) have shown that these networks have surpassed older algorithms like Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) in terms of accuracy, speed, and resources management. Even though that CNN have better accuracy and speed they still are heavy in resource consumption on computers which makes them not suitable to deploy on an embedded platform. This paper proposes a lean CNN that has a smaller number of parameters and still maintaining the best accuracy possible on vehicle classification.",
keywords = "Artificial Intelligence, CNN, Vehicle Classification, Engineering",
author = "Sanchez-Castro, {Jonathan J.} and Rodriguez-Quinonez, {Julio C.} and Ramirez-Hernandez, {Luis R.} and Guillermo Galaviz and Daniel Hernandez-Balbuena and Gabriel Trujillo-Hernandez and Wendy Flores-Fuentes and Paolo Mercorelli and Wilmar Hernandez-Perdomo and Oleg Sergiyenko and Gonzalez-Navarro, {Felix Fernando}",
year = "2020",
month = jun,
day = "1",
doi = "10.1109/ISIE45063.2020.9152274",
language = "English",
isbn = "978-1-7281-5636-1",
series = "IEEE International Symposium on Industrial Electronics (ISIE)",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "1365--1369",
booktitle = "2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)",
address = "United States",
note = "29th IEEE International Symposium on Industrial Electronics, ISIE 2020, ISIE 2020 ; Conference date: 17-06-2020 Through 19-06-2020",
url = "http://isie2020.org/",
}
RIS
TY - CHAP
T1 - A Lean Convolutional Neural Network for Vehicle Classification
AU - Sanchez-Castro, Jonathan J.
AU - Rodriguez-Quinonez, Julio C.
AU - Ramirez-Hernandez, Luis R.
AU - Galaviz, Guillermo
AU - Hernandez-Balbuena, Daniel
AU - Trujillo-Hernandez, Gabriel
AU - Flores-Fuentes, Wendy
AU - Mercorelli, Paolo
AU - Hernandez-Perdomo, Wilmar
AU - Sergiyenko, Oleg
AU - Gonzalez-Navarro, Felix Fernando
N1 - Conference code: 29
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Image classification is an important task in machine vision, in which vehicle classification is used for different applications like traffic analysis, autonomous driving, security, among others. Recent studies made with Convolutional Neural Networks (CNN) have shown that these networks have surpassed older algorithms like Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) in terms of accuracy, speed, and resources management. Even though that CNN have better accuracy and speed they still are heavy in resource consumption on computers which makes them not suitable to deploy on an embedded platform. This paper proposes a lean CNN that has a smaller number of parameters and still maintaining the best accuracy possible on vehicle classification.
AB - Image classification is an important task in machine vision, in which vehicle classification is used for different applications like traffic analysis, autonomous driving, security, among others. Recent studies made with Convolutional Neural Networks (CNN) have shown that these networks have surpassed older algorithms like Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) in terms of accuracy, speed, and resources management. Even though that CNN have better accuracy and speed they still are heavy in resource consumption on computers which makes them not suitable to deploy on an embedded platform. This paper proposes a lean CNN that has a smaller number of parameters and still maintaining the best accuracy possible on vehicle classification.
KW - Artificial Intelligence
KW - CNN
KW - Vehicle Classification
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85089497965&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/fe00f811-7a5d-326f-85ef-354bee8929b1/
U2 - 10.1109/ISIE45063.2020.9152274
DO - 10.1109/ISIE45063.2020.9152274
M3 - Article in conference proceedings
AN - SCOPUS:85089497965
SN - 978-1-7281-5636-1
T3 - IEEE International Symposium on Industrial Electronics (ISIE)
SP - 1365
EP - 1369
BT - 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 29th IEEE International Symposium on Industrial Electronics, ISIE 2020
Y2 - 17 June 2020 through 19 June 2020
ER -
