A Light Weight Multi-Head SSD Model for ADAS Applications

Chun Yu Lai, Bo Xun Wu, Tsung Han Lee, Vinay Malligere Shivanna, Jiun In Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Moving objects detection is considered as one of the prime safety indicators in the Advanced Driver Assistance System (ADAS). For implementing on resource-limited embedded platforms and still yield sufficient frame rate and quality, the paper proposes a lightweight multi-head single shot detector (SSD) model that strengthens the moving object detection significantly. The paper also introduces focal loss method to deal with imbalance problem of detecting pedestrians and bikes in training datasets (vehicles, bikes, and pedestrians). Lastly, the proposed lightweight network can be deployed on low-power embedded devices to achieve real-time processing performance (512x256) yielding 30fps.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
CountryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • Advanced driver assistance system (ADAS)
  • Imbalance dataset
  • Object detection

Fingerprint Dive into the research topics of 'A Light Weight Multi-Head SSD Model for ADAS Applications'. Together they form a unique fingerprint.

Cite this