InfraParis: A multi-modal and multi-task autonomous driving dataset

Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handle new objects, noise, nighttime conditions, and diverse scenarios, which is esse...

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Bibliographic Details
Published in2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) pp. 2961 - 2971
Main Authors Franchi, Gianni, Hariat, Marwane, Yu, Xuanlong, Belkhir, Nacim, Manzanera, Antoine, Filliat, David
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.01.2024
Subjects
Algorithms
Applications
Autonomous Driving
Computational modeling
Computer vision
Data models
Datasets and evaluations
Multitasking
Object detection
Semantic segmentation
Uncertainty
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Summary:Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handle new objects, noise, nighttime conditions, and diverse scenarios, which is essential for safety-critical applications. Despite ongoing efforts to enhance the resilience of computer vision DNNs, progress has been sluggish, partly due to the absence of benchmarks featuring multiple modalities. We introduce a novel and versatile dataset named InfraParis that supports multiple tasks across three modalities: RGB, depth, and infrared. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation. More visualizations and the download link for InfraParis are available at https://enstau2is.github.io/infraParis/.
ISSN:2642-9381
DOI:10.1109/WACV57701.2024.00295