Real Time Object Detection Using FPGA

In today’s rapidly advancing world, remarkable technological advancements are happening every minute in every field. One area that has experienced a massive transformation is the convergence of Embedded systems, the Internet of Things (IoT), and cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and computer vision. These advancements have led to significant progress in the field of image detection, with object detection emerging as one of the most successful breakthroughs in computer vision. When it comes to implementing image detection, experts often debate the effectiveness of using AI-Python models versus Field Programmable Gate Arrays (FPGAs). Both AI-Python models and FPGAs possess unique strengths and can be highly effective in different scenarios. Recognizing the value of exploring different approaches, our embedded team embarked on a Proof of Concept (PoC) journey. The goal of this project was to explore how real-time object detection using FPGA utilized to enhance object detection by improving speed and efficiency in comparison to traditional methods that is using AI-based Python models. The primary objective of our PoC was to implement the Single Shot Detector (SSD) model on an FPGA and compare the results obtained with running the same model on a CPU. By focusing on the implementation of real-time object detection using FPGA, our team aimed to harness the power of FPGAs to enhance real-time vision systems. This would enable us to facilitate rapid and precise object detection in a wide range of applications.