  The edge computing paradigm places compute-capable devices - edge servers -
at the network edge to assist mobile devices in executing data analysis tasks.
Intuitively, offloading compute-intense tasks to edge servers can reduce their
execution time. However, poor conditions of the wireless channel connecting the
mobile devices to the edge servers may degrade the overall capture-to-output
delay achieved by edge offloading. Herein, we focus on edge computing
supporting remote object detection by means of Deep Neural Networks (DNNs), and
develop a framework to reduce the amount of data transmitted over the wireless
link. The core idea we propose builds on recent approaches splitting DNNs into
sections - namely head and tail models - executed by the mobile device and edge
server, respectively. The wireless link, then, is used to transport the output
of the last layer of the head model to the edge server, instead of the DNN
input. Most prior work focuses on classification tasks and leaves the DNN
structure unaltered. Herein, our focus is on DNNs for three different object
detection tasks, which present a much more convoluted structure, and modify the
architecture of the network to: (i) achieve in-network compression by
introducing a bottleneck layer in the early layers on the head model, and (ii)
prefilter pictures that do not contain objects of interest using a
convolutional neural network. Results show that the proposed technique
represents an effective intermediate option between local and edge computing in
a parameter region where these extreme point solutions fail to provide
satisfactory performance. The code and trained models are available at
https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors .
