A Low-Latency Object Detection Algorithm for the Edge Devices of IoV Systems

Cheng Dai, Xingang Liu, Weiting Chen, Chin Feng Lai

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.

Original languageEnglish
Article number9137393
Pages (from-to)11169-11178
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number10
DOIs
Publication statusPublished - 2020 Oct

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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