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METHOD AND SYSTEM OF IMAGE BASED ANOMALY LOCALIZATION FOR VEHICLES THROUGH GENERATIVE CONTEXTUALIZED ADVERSARIAL NETWORK

机译:基于生成式情境化对抗网络的车辆图像异常定位方法与系统

摘要

The present invention provides an anomaly detection method and apparatus based on a neural network which can be trained on undamaged normal vehicle images and able to detect unknown/unseen vehicle damages of stochastic types and extents from images which are taken in various contexts. The provided method and apparatus are implemented with functional units which are trained to perform the anomaly detection under a GCAN model with a training dataset containing images of undamaged vehicles, intact-vehicle frame images and augmented vehicle frame images of the vehicles.
机译:本发明提供了一种基于神经网络的异常检测方法和装置,该神经网络可以在未损坏的正常车辆图像上进行训练,并且能够从各种环境中拍摄的图像中检测未知/未看到的随机类型和范围的车辆损坏。所提供的方法和装置通过功能单元来实现,这些功能单元经过训练以在GCAN模型下执行异常检测,该GCAN模型具有训练数据集,该训练数据集包含未损坏车辆的图像、完整车辆框架图像和车辆的增广车辆框架图像。

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