公开(公告)号：US10262237B2

公开(公告)日：2019-04-16

申请号：US15372953

申请日：2016-12-08

Applicant: Intel Corporation

Inventor： Byungseok Roh ,  Kye-Hyeon Kim ,  Sanghoon Hong ,  Minje Park ,  Yeongjae Cheon

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04

Abstract: Technologies for multi-scale object detection include a computing device including a multi-layer convolution network and a multi-scale region proposal network (RPN). The multi-layer convolution network generates a convolution map based on an input image. The multi-scale RPN includes multiple RPN layers, each with a different receptive field size. Each RPN layer generates region proposals based on the convolution map. The computing device may include a multi-scale object classifier that includes multiple region of interest (ROI) pooling layers and multiple associated fully connected (FC) layers. Each ROI pooling layer has a different output size, and each FC layer may be trained for an object scale based on the output size of the associated ROI pooling layer. Each ROI pooling layer may generate pooled ROIs based on the region proposals and each FC layer may generate object classification vectors based on the pooled ROIs. Other embodiments are described and claimed.

公开(公告)号：US20180165551A1

公开(公告)日：2018-06-14

申请号：US15372953

申请日：2016-12-08

Applicant: Intel Corporation

Inventor： Byungseok Roh ,  Kye-Hyeon Kim ,  Sanghoon Hong ,  Minje Park ,  Yeongjae Cheon

IPC: G06K9/62 ,  G06T7/00 ,  G06N3/08

CPC classification number: G06K9/3233 ,  G06K9/4628 ,  G06N3/0454

Abstract: Technologies for multi-scale object detection include a computing device including a multi-layer convolution network and a multi-scale region proposal network (RPN). The multi-layer convolution network generates a convolution map based on an input image. The multi-scale RPN includes multiple RPN layers, each with a different receptive field size. Each RPN layer generates region proposals based on the convolution map. The computing device may include a multi-scale object classifier that includes multiple region of interest (ROI) pooling layers and multiple associated fully connected (FC) layers. Each ROI pooling layer has a different output size, and each FC layer may be trained for an object scale based on the output size of the associated ROI pooling layer. Each ROI pooling layer may generate pooled ROIs based on the region proposals and each FC layer may generate object classification vectors based on the pooled ROIs. Other embodiments are described and claimed.