  Applying deep learning to object detection provides the capability to
accurately detect and classify complex objects in the real world. However,
currently, few mobile applications use deep learning because such technology is
computation-intensive and energy-consuming. This paper, to the best of our
knowledge, presents the first detailed experimental study of a mobile augmented
reality (AR) client's energy consumption and the detection latency of executing
Convolutional Neural Networks (CNN) based object detection, either locally on
the smartphone or remotely on an edge server. In order to accurately measure
the energy consumption on the smartphone and obtain the breakdown of energy
consumed by each phase of the object detection processing pipeline, we propose
a new measurement strategy. Our detailed measurements refine the energy
analysis of mobile AR clients and reveal several interesting perspectives
regarding the energy consumption of executing CNN-based object detection.
Furthermore, several insights and research opportunities are proposed based on
our experimental results. These findings from our experimental study will guide
the design of energy-efficient processing pipeline of CNN-based object
detection.
