Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.
КЛАССИФИКАТОРЫ НИЗКОГО И ВЫСОКОГО КАЧЕСТВА, ПРИМЕНЯЕМЫЕ К ИЗОБРАЖЕНИЯМ ДОРОЖНЫХ СЦЕН
2018-07-18
Patent
Elektronische Ressource
Russisch
ГАЗОТУРБИННЫЙ ДВИГАТЕЛЬ С ТУРБИНОЙ НИЗКОГО ДАВЛЕНИЯ, СОДЕРЖАЩЕЙ НЕБОЛЬШОЕ ЧИСЛО СТУПЕНЕЙ
Europäisches Patentamt | 2016
Разработка базы данных для автоматизации контроля, установки и обслуживания дорожных знаков
DOAJ | 2017
|ИНТРАОПЕРАЦИОННЫЙ МОНИТОРИНГ КАЧЕСТВА СИСТЕМ ОТСЛЕЖИВАНИЯ
Europäisches Patentamt | 2016