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Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis

Research output: Contribution to journalArticle

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Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. / Tai, Shao-Kuo; Dewi, Christine; Chen, Rung-Ching; Liu, Yan-Ting; Jiang, Xiaoyi; Yu, Hui.

In: Applied Sciences (Switzerland), Vol. 10, No. 19, 6997, 07.10.2020.

Research output: Contribution to journalArticle

Harvard

Tai, S-K, Dewi, C, Chen, R-C, Liu, Y-T, Jiang, X & Yu, H 2020, 'Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis', Applied Sciences (Switzerland), vol. 10, no. 19, 6997. https://doi.org/10.3390/app10196997

APA

Tai, S-K., Dewi, C., Chen, R-C., Liu, Y-T., Jiang, X., & Yu, H. (2020). Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. Applied Sciences (Switzerland), 10(19), [6997]. https://doi.org/10.3390/app10196997

Vancouver

Tai S-K, Dewi C, Chen R-C, Liu Y-T, Jiang X, Yu H. Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. Applied Sciences (Switzerland). 2020 Oct 7;10(19). 6997. https://doi.org/10.3390/app10196997

Author

Tai, Shao-Kuo ; Dewi, Christine ; Chen, Rung-Ching ; Liu, Yan-Ting ; Jiang, Xiaoyi ; Yu, Hui. / Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. In: Applied Sciences (Switzerland). 2020 ; Vol. 10, No. 19.

Bibtex

@article{1b1494b126a4472f9c93c42b1b6323ed,
title = "Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis",
abstract = "In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3{\textquoteright}s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.",
keywords = "TSD, object detection, TSR, Yolo V3, SPP, scale analysis",
author = "Shao-Kuo Tai and Christine Dewi and Rung-Ching Chen and Yan-Ting Liu and Xiaoyi Jiang and Hui Yu",
year = "2020",
month = oct,
day = "7",
doi = "10.3390/app10196997",
language = "English",
volume = "10",
journal = "Applied Sciences",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "19",

}

RIS

TY - JOUR

T1 - Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis

AU - Tai, Shao-Kuo

AU - Dewi, Christine

AU - Chen, Rung-Ching

AU - Liu, Yan-Ting

AU - Jiang, Xiaoyi

AU - Yu, Hui

PY - 2020/10/7

Y1 - 2020/10/7

N2 - In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.

AB - In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.

KW - TSD

KW - object detection

KW - TSR

KW - Yolo V3

KW - SPP

KW - scale analysis

U2 - 10.3390/app10196997

DO - 10.3390/app10196997

M3 - Article

VL - 10

JO - Applied Sciences

JF - Applied Sciences

SN - 2076-3417

IS - 19

M1 - 6997

ER -

ID: 22978901