Traffic light detection by camera is a challenging task for autonomous driving mainly due to the small size of traffic lights in the road scene especially for early detection. The limited resolution in the corresponding area of traffic lights reduces their contrast to the background, as well as the effectiveness of the visual cues from the traffic light itself. We believe understanding the scene semantics between traffic lights and their surroundings can play a vital role in tackling this challenge. Towards this goal, we build a generative adversarial network (GAN) model to predict the existence of traffic lights from the road scene image where existing traffic lights are removed with image inpainting. Using Cityscape dataset [2], we verify that the proposed GAN model indeed captures the desired semantics by showing effective predictions of existence of traffic lights that are consistent with real images. Moreover, we leverage this model to augment the training data where traffic lights are inserted to the road scene images based on the prediction of the GAN model. While the augmented images may not be realistic looking, results show that such data augmentation can improve the traffic light detector performance that is comparable to using additional real data collection, and better than other data augmentation with various randomization schemes. These results verify the importance of semantic consistency for data augmentation to improve the traffic light detection.


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    Title :

    Semantic Consistency: The Key to Improve Traffic Light Detection with Data Augmentation


    Contributors:
    Hassan, Eman T. (author) / Li, Nanxiang (author) / Ren, Liu (author)


    Publication date :

    2020-10-19


    Size :

    2709274 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SEMANTIC CONSISTENCY: THE KEY TO IMPROVE TRAFFIC LIGHT DETECTION WITH DATA AUGMENTATION

    Hassan, Eman T. / Li, Nanxiang / Ren, Liu | British Library Conference Proceedings | 2020





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