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

Abstract

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.

Keywords

• Computer vision
• deep learning
• machine learning
• object detection