Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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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/works › Article in conference proceedings › Research › peer-review
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RIS
TY - CHAP
T1 - Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles
T2 - 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
AU - Faßmeyer, Pascal
AU - Kortmann, Felix
AU - Drews, Paul
AU - Funk, Burkhardt
N1 - Conference code: 94
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
UR - http://www.scopus.com/inward/record.url?scp=85122992755&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/1bca900b-296a-33a1-9b3e-333618b4c84b/
U2 - 10.1109/VTC2021-Fall52928.2021.9625213
DO - 10.1109/VTC2021-Fall52928.2021.9625213
M3 - Article in conference proceedings
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.
CY - Piscataway
Y2 - 27 September 2021 through 28 September 2021
ER -