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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12359)

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.

Keywords

Detection Unsupervised domain adaptation Adverse weather Rain Haze 

Notes

Acknowledgement

This work was supported by the NSF grant 1910141.

Supplementary material

504468_1_En_45_MOESM1_ESM.pdf (2.6 mb)
Supplementary material 1 (pdf 2658 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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