FPGA implementation of real-time target detection algorithm based on YOLOv3 model

• Main Author: Zhang, Tuo
• Other Authors: Zheng Yuanjin
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Engineering; Deep learning; Object detection; YOLOv3; FPGA
• Online Access: https://hdl.handle.net/10356/182406
• Institution: Nanyang Technological University

With the increasing number of cars in the society and the increasing incidence of traffic accidents, the demand for vehicle visual assistant driving is increasing.Object detection is the core component of vehicle vision assisted driving system. In recent years, deep learning has developed rapidly, and the recognition accuracy and speed of object detection algorithm have been greatly improved.However, due to its complex structure, large amount of calculation and high requirements for hardware performance, the object detection algorithm based on deep learning is often difficult to achieve in engineering applications. This dissertation compares and analyzes the advantages and disadvantages of the current object detection algorithm based on deep learning. Compared with other mainstream object detection algorithms, YOLO series algorithm has the advantages of faster detection speed and better real-time performance. In the aspect of hardware implementation, compared with CPU, GPU and ASIC embedded hardware, FPGA has the advantages of low power consumption, programmability, parallelism and low cost. Aiming at the practical engineering application of the object detection algorithm in intelligent driving, this dissertation selects the appropriate embedded low-power hardware and deploys the best object detection algorithm. At the same time, we uses DP-8020 development board and DNNDK deep compression tool developed by Shenjian technology company to realize the real-time object detection of MobileNet-YOLOv3 object detection algorithm on FPGA.