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dc.contributor.authorNguyen, Hai Q.-
dc.contributor.authorTran, Vinh P.-
dc.contributor.authorNguyen, D. Vo-
dc.contributor.authorNguyen, Khang-
dc.descriptionScientific Paper; Pages: 1-8vi_VN
dc.description.abstractConvolutional Neural Networks (CNNs) are considerably developed year by year for better accuracy. New CNN architectures such as residual network (ResNet) are believed to replace the old ones (e.g. VGG) in all tasks. In this paper, we give evidence to prove that VGG is still useful for some specific tasks. We focus on training RetinaNet object detector on VisDrone dataset using ResNet and VGG as the backbone to detect four types of vehicle, which usually occupy small numbers of pixels in aerial images. From our experiments, we evaluate the effect of two mentioned CNN types on detecting small objects in images and undergo some incredible works from VGG – a sequential neural network.vi_VN
dc.publisherDa Nang Publishing Housevi_VN
dc.subjectDeep Learningvi_VN
dc.subjectSmall Object Detectionvi_VN
dc.subjectComputer Visionvi_VN
dc.subjectAerial Imagevi_VN
dc.titleDeep feature extractors for small object detection in aerial imagesvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:CITA 2019

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