OBJECT DETECTION ALGORITHMS IMPLEMENTATION ON EMBEDDED DEVICES: CHALLENGES AND SUGGESTED SOLUTIONS

Abstract

Object detection and image classification are among the most important areas to which scientific research is directed, which are commonly used in various applications based on computer vision. The development of low-cost embedded devices with powerful processing is leading to a trend in their use in computer vision, which provides reduced access time, reliability, and data security. That's why Tiny Machine Learning (TinyML) technology appeared, which is field that specifically explores the application of machine learning on highly limited edge devices. Deep learning techniques are increasingly employed in data-intensive and time-sensitive IoT applications. Deploying Deep Neural Network (DNN) models on microcontrollers (MCUs) is challenging due to limited resources such as RAM. Recent advancements in the field of TinyML hold the potential to introduce a novel category of peripheral applications. TinyML enables the development of new applications and services by eliminating the reliance on cloud computing, which consumes power and poses risks to data security and privacy. TinyML currently regarded as a promising artificial intelligence (AI( alternative that specifically targets technologies and applications for devices with very low profiles. In this paper, the most important algorithms used in object detection will be presented, in addition to the challenges in using low-resource embedded systems that support TinyML technology.

Keywords

• tiny machine learning
• deep learning; artificial intelligence
• embedded devices
• tensorflow lite
• micropython