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Prior-Based Domain Adaptive Object Detection for Hazy and Rainy Conditions

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12359))

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Abstract

Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these corrupted images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to hazy and rainy conditions. In particular, we use weather-specific prior knowledge obtained using the principles of image formation to define a novel prior-adversarial loss. The prior-adversarial loss, which we use to supervise the adaptation process, aims to reduce the weather-specific information in the features, thereby mitigating the effects of weather on the detection performance. Additionally, we introduce a set of residual feature recovery blocks in the object detection pipeline to de-distort the feature space, resulting in further improvements. Evaluations performed on various datasets (Foggy-Cityscapes, Rainy-Cityscapes, RTTS and UFDD) for rainy and hazy conditions demonstrates the effectiveness of the proposed approach.

V. A. Sindagi and P. Oza—Equal contribution.

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Acknowledgement

This work was supported by the NSF grant 1910141.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD, 21218, USA

    Vishwanath A. Sindagi, Poojan Oza, Rajeev Yasarla & Vishal M. Patel

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  1. Vishwanath A. Sindagi
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  2. Poojan Oza
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  3. Rajeev Yasarla
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  4. Vishal M. Patel
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Correspondence to Vishwanath A. Sindagi .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Sindagi, V.A., Oza, P., Yasarla, R., Patel, V.M. (2020). Prior-Based Domain Adaptive Object Detection for Hazy and Rainy Conditions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_45

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