Abstract
AbstractCar accident inspection is a binary classification task to recognize whether a given car image includes a damaged surface or not. While the prior studies utilized various computer vision algorithms under the fully supervised, high data availability regime, these studies bear several limits for application in the real world. First, acquiring a large amount of car accident images is challenging due to their scarcity. Second, the supervised classifier would fail to recognize a sample not seen a priori. To improve the aforementioned drawbacks, we propose a few-shot classification framework for the accident inspection task and illustrate several takeaways to the practitioners. First, we designed a few-shot classification framework and validated our approach precisely identifies the accident, although the practitioner has a few accident images. Second, we analyzed the fine-grained discriminative characteristics between normal and accident images; thus, fine-grained feature extractor architecture is adequate for our accident inspection task. Third, we scrutinized optimal image resizing strategy varies along with the feature extractor architecture; therefore, we recommend that practitioners be cautious in handling real world car images. Lastly, we analyzed a larger number of acquired accident images that are advantageous in a few-shot classification. Based on these contributions, we highly expect further studies can realize the benefits of automated car part recognition in the real world shortly.KeywordsCar accident inspectionFew-shot learningFine-grained classification
Published Version
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