Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations

By Georg Volk, Stefan Müller, Alexander von Bernuth, Dennis Hospach, Oliver Bringmann. 2019

91 citations recorded by the source corpus.

Convolutional Neural Networks (CNNs) achieve high accuracy in vision-based object detection tasks. For their usage in the automotive domain, CNNs have to be robust against various kinds of natural distortions caused by different weather conditions while state-of-the-art datasets like KITTI lack these challenging scenarios. Our approach automatically identifies corner cases where CNNs fail and improves their robustness by automated augmentation of the training data with synthetic rain variations including falling rain with brightness reduction as well as raindrops on the windshield. Our method achieves higher performance upon validation against a real rain dataset compared with state-of-the art data augmentation techniques like Gaussian noise (GN) or Salt-and-Pepper noise (SPN).

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

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Citations in the corpus

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019, background): “Evaluating robustness to environmental changes in autonomous driving In recent years, weather conditions turned out to be a central limitation for state-of-the art autonomous driving systems [Sakaridis et al., 2018a, Volk et al., 2019, Dai and Van Gool, 2018, Chen et al., 2018, Lee et al., 2018].”
In Search of Lost Domain Generalization (2020, background): “For instance, self-driving car systems struggle to perform under conditions different to those of training, including variations in light [Dai and Van Gool, 2018], weather [Volk et al., 2019], and object poses [Alcorn et al.”
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (2021, background): “Accuracy suffers when the model performs under conditions different to those of training, such as variations in light [8], weather [51], object poses [2], textures [16], or object backgrounds [5].”