Fast and Lightweight Object Detection Network: Detection and Recognition on Resource Constrained Devices

IEEE Access

Bernardo Augusto Godinho de Oliveira¹  Flavia Magalhaes Freitas Ferreira²  Carlos Augusto Paiva da Silva Martins³ 

[1] Pontif&x00ED;cia Universidade Cat&x00F3;lica de Minas Gerais, Belo Horizonte, Brasil;

关键词: Computer vision;    deep learning;    machine learning;    object detection;   

DOI  :  10.1109/ACCESS.2018.2801813

来源: DOAJ

【 摘 要 】

The intrinsic ability of humans to rapidly detect, differentiate, and classify objects allows us to make quick decisions in regards to what we see. Several appliances can make use of fast and lightweight automated object detection for images or videos. Throughout the last five years, the technology industry has constantly introduced computational and hardware solutions, such as devices with impressive processing and storage capabilities. However, object detection methods usually require either high processing power or large storage availability, making it hard for resource constrained devices to perform the detection in real-time without a connection to a powerful server. The model presented in this paper requires only 95 megabytes of storage and took 113 ms in average per image running on a laptop CPU, making it suitable for standalone devices that can be used on the go.

【 授权许可】

Unknown