Object detection is a major challenge in computer vision, involving both
object classification and object localization within a scene. While deep neural
networks have been shown in recent years to yield very powerful techniques for
tackling the challenge of object detection, one of the biggest challenges with
enabling such object detection networks for widespread deployment on embedded
devices is high computational and memory requirements. Recently, there has been
an increasing focus in exploring small deep neural network architectures for
object detection that are more suitable for embedded devices, such as Tiny YOLO
and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture
introduced in SqueezeNet and the object detection performance of the
single-shot detection macroarchitecture introduced in SSD, this paper
introduces Tiny SSD, a single-shot detection deep convolutional neural network
for real-time embedded object detection that is composed of a highly optimized,
non-uniform Fire sub-network stack and a non-uniform sub-network stack of
highly optimized SSD-based auxiliary convolutional feature layers designed
specifically to minimize model size while maintaining object detection
performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller
than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher
than Tiny YOLO). These experimental results show that very small deep neural
network architectures can be designed for real-time object detection that are
well-suited for embedded scenarios.

本文提出了一种基于深度卷积神经网络的实时嵌入式物体检测算法 Tiny SSD，通过高度优化的非均匀 Fire 子网堆栈和高度优化的非均匀 SSD 辅助卷积特征层，将模型大小控制在 2.3MB (~ 比 Tiny YOLO 小 26 倍) 的情况下，仍然能够在 VOC 2007 上实现 61.3％（比 Tiny YOLO 高 4.2％）的 mean average precision (mAP)，结果表明可以为嵌入式场景设计非常小的深度神经网络体系结构以进行实时物体检测。

小型 SSD：一个用于实时嵌入式对象检测的小型单次拍摄检测深度卷积神经网络

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network  for Real-time Embedded Object Detection

Object detection is considered one of the most challenging problems in this
field of computer vision, as it involves the combination of object
classification and object localization within a scene. Recently, deep neural
networks (DNNs) have been demonstrated to achieve superior object detection
performance compared to other approaches, with YOLOv2 (an improved You Only
Look Once model) being one of the state-of-the-art in DNN-based object
detection methods in terms of both speed and accuracy. Although YOLOv2 can
achieve real-time performance on a powerful GPU, it still remains very
challenging for leveraging this approach for real-time object detection in
video on embedded computing devices with limited computational power and
limited memory. In this paper, we propose a new framework called Fast YOLO, a
fast You Only Look Once framework which accelerates YOLOv2 to be able to
perform object detection in video on embedded devices in a real-time manner.
First, we leverage the evolutionary deep intelligence framework to evolve the
YOLOv2 network architecture and produce an optimized architecture (referred to
as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To
further reduce power consumption on embedded devices while maintaining
performance, a motion-adaptive inference method is introduced into the proposed
Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2
based on temporal motion characteristics. Experimental results show that the
proposed Fast YOLO framework can reduce the number of deep inferences by an
average of 38.13%, and an average speedup of ~3.3X for objection detection in
video compared to the original YOLOv2, leading Fast YOLO to run an average of
~18FPS on a Nvidia Jetson TX1 embedded system.

本文提出了一种名为 Fast YOLO 的新框架，通过深度智能进化框架优化了 YOLOv2 网络架构并将运动自适应推理方法引入，从而在保持性能的同时，在嵌入式设备上实现实时物体检测。实验结果表明，Fast YOLO 框架可以在平均 3.3 倍的速度加速下，将嵌入式系统上的检测性能提高到 18FPS。

快速 YOLO: 一种实时嵌入式视频目标检测仅需一次看图系统

Fast YOLO: A Fast You Only Look Once System for Real-time Embedded  Object Detection in Video

Recent advances in deep learning motivate the use of deep neutral networks in
sensing applications, but their excessive resource needs on constrained
embedded devices remain an important impediment. A recently explored solution
space lies in compressing (approximating or simplifying) deep neural networks
in some manner before use on the device. We propose a new compression solution,
called DeepIoT, that makes two key contributions in that space. First, unlike
current solutions geared for compressing specific types of neural networks,
DeepIoT presents a unified approach that compresses all commonly used deep
learning structures for sensing applications, including fully-connected,
convolutional, and recurrent neural networks, as well as their combinations.
Second, unlike solutions that either sparsify weight matrices or assume linear
structure within weight matrices, DeepIoT compresses neural network structures
into smaller dense matrices by finding the minimum number of non-redundant
hidden elements, such as filters and dimensions required by each layer, while
keeping the performance of sensing applications the same. Importantly, it does
so using an approach that obtains a global view of parameter redundancies,
which is shown to produce superior compression. We conduct experiments with
five different sensing-related tasks on Intel Edison devices. DeepIoT
outperforms all compared baseline algorithms with respect to execution time and
energy consumption by a significant margin. It reduces the size of deep neural
networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to
94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements
are achieved without loss of accuracy. The results underscore the potential of
DeepIoT for advancing the exploitation of deep neural networks on
resource-constrained embedded devices.

DeepIoT 是一种压缩神经网络的解决方案，它通过找到每个层所需的最小数量的非冗余隐藏元素，将神经网络结构压缩成更小的密集矩阵，同时保持感知应用程序的性能。在 Intel Edison 设备上的五个不同的感知相关任务中进行实验，DeepIoT 在执行时间和能源消耗方面优于所有比较的基线算法，而不会损失准确性。