Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN. / Faßmeyer, Pascal; Kortmann, Felix; Drews, Paul et al.
2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings: Proceedings, Virtual Conference, 27 - 30 September 2021. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2021. (IEEE Vehicular Technology Conference; Vol. 94).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Faßmeyer, P, Kortmann, F, Drews, P & Funk, B 2021, Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN. in 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings: Proceedings, Virtual Conference, 27 - 30 September 2021. IEEE Vehicular Technology Conference, vol. 94, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), Norman, United States, 27.09.21. https://doi.org/10.1109/VTC2021-Fall52928.2021.9625213

APA

Faßmeyer, P., Kortmann, F., Drews, P., & Funk, B. (2021). Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN. In 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings: Proceedings, Virtual Conference, 27 - 30 September 2021 (IEEE Vehicular Technology Conference; Vol. 94). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2021-Fall52928.2021.9625213

Vancouver

Faßmeyer P, Kortmann F, Drews P, Funk B. Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN. In 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings: Proceedings, Virtual Conference, 27 - 30 September 2021. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2021. (IEEE Vehicular Technology Conference). doi: 10.1109/VTC2021-Fall52928.2021.9625213

Bibtex

@inbook{c42e37af4b2245f3828e00945fc98050,
title = "Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN",
abstract = "Initiatives such as the 2020 IEEE Global Road Damage Detection Challenge prompted extensive research in camera-based road damage detection with Deep Learning, primarily focused on improving the efficiency of road management. However, road damage detection is also relevant for automated driving to optimize passenger comfort and safety. We use the state-of-the-art object detection framework Scaled-YOLOv4 and develop two small-sized models that cope with the limited computational resources in the vehicle. With average F1 scores of 0.54 and 0.586, respectively, the models keep pace with the state-of-the-art solutions of the challenge. Since the data consists only of smartphone images, we also train expert models for autonomous driving utilizing vehicle camera data. In addition to detection, severity assessment is critical. We propose a semi-supervised learning approach based on the encodings learned by combining a class-conditional Variational Autoencoder and a Wasserstein Generative Adversarial Network to classify detected damage into different severity levels.",
keywords = "Business informatics, road damage, deep learning, Computer vision, autonomous driving, scaled-YOLOv4, VAE, Wasserstein GAN",
author = "Pascal Fa{\ss}meyer and Felix Kortmann and Paul Drews and Burkhardt Funk",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) ; Conference date: 27-09-2021 Through 28-09-2021",
year = "2021",
month = sep,
day = "1",
doi = "10.1109/VTC2021-Fall52928.2021.9625213",
language = "English",
isbn = "978-1-6654-1369-5",
series = "IEEE Vehicular Technology Conference",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings",
address = "United States",
url = "https://events.vtsociety.org/vtc2021-fall/",

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RIS

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AU - Faßmeyer, Pascal

AU - Kortmann, Felix

AU - Drews, Paul

AU - Funk, Burkhardt

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PY - 2021/9/1

Y1 - 2021/9/1

N2 - Initiatives such as the 2020 IEEE Global Road Damage Detection Challenge prompted extensive research in camera-based road damage detection with Deep Learning, primarily focused on improving the efficiency of road management. However, road damage detection is also relevant for automated driving to optimize passenger comfort and safety. We use the state-of-the-art object detection framework Scaled-YOLOv4 and develop two small-sized models that cope with the limited computational resources in the vehicle. With average F1 scores of 0.54 and 0.586, respectively, the models keep pace with the state-of-the-art solutions of the challenge. Since the data consists only of smartphone images, we also train expert models for autonomous driving utilizing vehicle camera data. In addition to detection, severity assessment is critical. We propose a semi-supervised learning approach based on the encodings learned by combining a class-conditional Variational Autoencoder and a Wasserstein Generative Adversarial Network to classify detected damage into different severity levels.

AB - Initiatives such as the 2020 IEEE Global Road Damage Detection Challenge prompted extensive research in camera-based road damage detection with Deep Learning, primarily focused on improving the efficiency of road management. However, road damage detection is also relevant for automated driving to optimize passenger comfort and safety. We use the state-of-the-art object detection framework Scaled-YOLOv4 and develop two small-sized models that cope with the limited computational resources in the vehicle. With average F1 scores of 0.54 and 0.586, respectively, the models keep pace with the state-of-the-art solutions of the challenge. Since the data consists only of smartphone images, we also train expert models for autonomous driving utilizing vehicle camera data. In addition to detection, severity assessment is critical. We propose a semi-supervised learning approach based on the encodings learned by combining a class-conditional Variational Autoencoder and a Wasserstein Generative Adversarial Network to classify detected damage into different severity levels.

KW - Business informatics

KW - road damage

KW - deep learning

KW - Computer vision

KW - autonomous driving

KW - scaled-YOLOv4

KW - VAE

KW - Wasserstein GAN

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DO - 10.1109/VTC2021-Fall52928.2021.9625213

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SN - 978-1-6654-1369-5

T3 - IEEE Vehicular Technology Conference

BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

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