Object detection is one of the key tasks in many applications of computer
vision. Deep Neural Networks (DNNs) are undoubtedly a well-suited approach for
object detection. However, such DNNs need highly adapted hardware together with
hardware-specific optimization to guarantee high efficiency during inference.
This is especially the case when aiming for efficient object detection in video
streaming applications on limited hardware such as edge devices. Comparing
vendor-specific hardware and related optimization software pipelines in a fair
experimental setup is a challenge. In this paper, we propose a framework that
uses a host computer with a host software application together with a
light-weight interface based on the Message Queuing Telemetry Transport (MQTT)
protocol. Various different target devices with target apps can be connected
via MQTT with this host computer. With well-defined and standardized MQTT
messages, object detection results can be reported to the host computer, where
the results are evaluated without harming or influencing the processing on the
device. With this quite generic framework, we can measure the object detection
performance, the runtime, and the energy efficiency at the same time. The
effectiveness of this framework is demonstrated in multiple experiments that
offer deep insights into the optimization of DNNs.

本文提出了一种基于 MQTT 协议的轻量级接口框架，可用于测量目标检测性能、运行时间和能量效率，特别适用于限制硬件上的视频流应用，为优化 DNNs 提供深入的见解。