  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.
