Deep Learning Model on Embedded Board for Vehicle Detection and Vehicle Tracking

• 발행기관: 한국스마트미디어학회
• 저자명: Luong Thanh Tra Nguyen Minh Nguyen Jongtae Lim Hyungsik Shin Seongwon Cho
• 간행물 정보: 『스마트미디어저널』제14권 제2호, 43~52쪽, 전체 10쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2025.02.28

국문 초록

This paper proposes a deep learning model to detect and track the vehicle on an embedded device such as Odroid, Orange Pi, etc. This system includes two main parts: vehicle detection and vehicle tracking. Since deep learning has achieved high accuracy over the classical image processing method, object detectors can detect vehicles in the street and highway. It can be normal to run the computer detection program with graphic processor unit (GPU) support, but it is challenging to run it on the embedded board with no GPU support and low central processing unit (CPU) performance. This paper focuses on balancing edge-computing-based deep learning object detection's accuracy and performance using additional techniques such as quantization, edge TPU, and multiple threads. SSDLite with MobileNet backbone is chosen due to its lighter than other networks but still obtain good performance compare with Yolo.

목차

Ⅰ. Introduction

Ⅱ. Related Research

Ⅲ. Optimization

Ⅳ. Experimental Results

Ⅴ. Conclusion

REFERENCES

키워드

Optimize model Vehicle detection Vehicle tracking Embedded device SSDLite MobileNet