论文标题
Mobiledensenet：移动设备上对象检测的一种新方法
MobileDenseNet: A new approach to object detection on mobile devices
论文作者
论文摘要
在过去的几年中，对象检测问题解决方案已经大大发展。在存在硬件限制的情况下，需要更轻的模型，以及对移动设备量身定制的模型的需求。在本文中，我们将评估创建解决这些问题的算法时使用的方法。本文的主要目标是提高最先进算法的准确性，同时保持速度和实时效率。一个阶段对象检测中最重要的问题与小物体有关和本地化不准确。作为解决方案，我们创建了一个新网络，名称为Mobiledensenet适合嵌入式系统。我们还开发了一种用于移动设备的轻颈FCPNLITE，可以帮助检测小物体。我们的研究表明，很少有论文引用嵌入式系统中的颈部。我们网络与其他网络的区别是我们使用串联功能。网络头部的一个小而显着的变化使精度放大了，而没有增加速度或限制参数。简而言之，我们对具有挑战性的可可和Pascal VOC数据集的关注分别为24.8和76.8，分别为百分比，这是迄今为止其他最先进系统所记录的率。我们的网络能够提高准确性，同时保持移动设备上的实时效率。我们将像素3（Snapdragon 845）上的操作速度计算为22.8 fps。该研究的源代码可在https://github.com/hajizadeh/mobiledensenet上获得。
Object detection problem solving has developed greatly within the past few years. There is a need for lighter models in instances where hardware limitations exist, as well as a demand for models to be tailored to mobile devices. In this article, we will assess the methods used when creating algorithms that address these issues. The main goal of this article is to increase accuracy in state-of-the-art algorithms while maintaining speed and real-time efficiency. The most significant issues in one-stage object detection pertains to small objects and inaccurate localization. As a solution, we created a new network by the name of MobileDenseNet suitable for embedded systems. We also developed a light neck FCPNLite for mobile devices that will aid with the detection of small objects. Our research revealed that very few papers cited necks in embedded systems. What differentiates our network from others is our use of concatenation features. A small yet significant change to the head of the network amplified accuracy without increasing speed or limiting parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets were 24.8 and 76.8 in percentage terms respectively - a rate higher than that recorded by other state-of-the-art systems thus far. Our network is able to increase accuracy while maintaining real-time efficiency on mobile devices. We calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The source code of this research is available on https://github.com/hajizadeh/MobileDenseNet.
