  In the past few years, numerous Deep Neural Network (DNN) models and
frameworks have been developed to tackle the problem of real-time object
detection from RGB images. Ordinary object detection approaches process
information from the images only, and they are oblivious to the camera pose
with regard to the environment and the scale of the environment. On the other
hand, mobile Augmented Reality (AR) frameworks can continuously track a
camera's pose within the scene and can estimate the correct scale of the
environment by using Visual-Inertial Odometry (VIO). In this paper, we propose
a novel approach that combines the geometric information from VIO with semantic
information from object detectors to improve the performance of object
detection on mobile devices. Our approach includes three components: (1) an
image orientation correction method, (2) a scale-based filtering approach, and
(3) an online semantic map. Each component takes advantage of the different
characteristics of the VIO-based AR framework. We implemented the AR-enhanced
features using ARCore and the SSD Mobilenet model on Android phones. To
validate our approach, we manually labeled objects in image sequences taken
from 12 room-scale AR sessions. The results show that our approach can improve
on the accuracy of generic object detectors by 12% on our dataset.
