Geometric property-based convolutional neural network for indoor object detection

International Journal of Advanced Robotic Systems

article

Xintao Ding¹  Boquan Li²  Jinbao Wang¹ 

[1] School of Computer and Information, Anhui Normal University;Anhui Province Key Laboratory of Network and Information Security;School of Mathematics and Statistics, Anhui Normal University

关键词: Indoor object detection;    robot application;    geometric constraint;    CNN;   

DOI  :  10.1177/1729881421993323

学科分类：社会科学、人文和艺术（综合）

来源: InTech

【 摘 要 】

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

【 授权许可】

CC BY