@inproceedings{c316c807ebc5418481d3d8b664522343,
title = "A Light Weight Multi-Head SSD Model for ADAS Applications",
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. ",
keywords = "Advanced driver assistance system (ADAS), Imbalance dataset, Object detection",
author = "Lai, {Chun Yu} and Wu, {Bo Xun} and Lee, {Tsung Han} and Shivanna, {Vinay Malligere} and Guo, {Jiun In}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 ; Conference date: 03-12-2020 Through 05-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICPAI51961.2020.00042",
language = "English",
series = "Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020",
address = "United States",
}