Implementation of Object Detection Using FPGA

Abstract

Robotics, autonomous driving, surveillance, and many more fields rely on object detection, a basic job in computer vision. Due to their low-latency speed and parallel processing capabilities, FPGA systems are attracting more and more interest in implementing object detection algorithms, which is important because real-time processing is becoming increasingly vital. This work provides a synopsis of the object detection on FPGA architecture, optimisation, and real-time implementation. The suggested method is picking an appropriate object detecting algorithm, like the well-known YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), which are renowned for their speed and accuracy ratio. To achieve real-time speed, the algorithm is mapped onto an FPGA-based hardware architecture, which takes use of its reconfigurability and parallelism. An essential part of FPGA-based object detection is the design of the hardware architecture. Optimisation of data pathways, construction of efficient control logic, and splitting of the algorithm into hardware-friendly components are all part of this process. To achieve the goal of maximising throughput with minimal resource use, techniques including parallel processing, loop unrolling, and pipelining are utilised. In addition, optimising for FPGA requires tweaking the algorithm and hardware design to make the most of the target FPGA device's capabilities. Reducing latency and increasing throughput requires optimising data transfer, parallelism, and memory access patterns. Another important part of object detection systems that use FPGAs is their ability to integrate with various sensors or input streams. Acquiring input data for real-time processing necessitates integrating with various sensors, such as cameras and LiDAR devices. Thanks to their adaptability, FPGA platforms may be easily integrated into a wide range of application situations, thanks to their ability to interface with different sensors. To ensure the object detection system built on FPGA is accurate, fast, and resilient, it is validated and tested using common datasets and real-world scenarios. To guarantee the system achieves the targeted performance metrics, the real-time processing requirements are thoroughly assessed. The FPGA-based object detection system, once tested, can be placed in the intended setting as either a standalone device or a component of a bigger embedded system. Fixing bugs, improving performance, and adding new features all require regular maintenance and upgrades.