Object detection for autonomous vehicle with Lidar using deep learning

Object detection for autonomous vehicle with Lidar using deep learning

This paper presents an object detection for Autonomous Vehicle (AV) using deep learning algorithm. Currently, most AVs use the camera for visualization to detect surrounding objects. However, the performance of a sensor, such as a camera with visual perception, is diminished in dim light, for instan...

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发表在:2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings
主要作者: 2-s2.0-85098280266
格式: Conference paper
语言:English
出版: Institute of Electrical and Electronics Engineers Inc. 2020
在线阅读:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098280266&doi=10.1109%2fICSET51301.2020.9265358&partnerID=40&md5=d7b18e9c18dadd6a6354bbf1ae3383bd
id Yahya M.A.; Abdul-Rahman S.; Mutalib S.
spelling Yahya M.A.; Abdul-Rahman S.; Mutalib S.
2-s2.0-85098280266
Object detection for autonomous vehicle with Lidar using deep learning
2020
2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings


10.1109/ICSET51301.2020.9265358
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098280266&doi=10.1109%2fICSET51301.2020.9265358&partnerID=40&md5=d7b18e9c18dadd6a6354bbf1ae3383bd
This paper presents an object detection for Autonomous Vehicle (AV) using deep learning algorithm. Currently, most AVs use the camera for visualization to detect surrounding objects. However, the performance of a sensor, such as a camera with visual perception, is diminished in dim light, for instance at night-time due to the less light environment. Thus, the study attempts to employ the Light Detection and Ranging (LiDAR) sensor that uses light in the form of a pulsed laser to calculate ranges and ultimately detect objects. The use of LiDAR with the recent deep learning algorithm, namely You Only Look Once (YOLO) v2, was simulated on the Robot Operating System (ROS) in the Linux environment. The collected data has undergone several filtering processes, which includes noise removal, downsampling, and transformation. The study then applies the model on real-time data from the LiDAR sensor to perform object detection. The results show that YOLOv2 can identify the objects better compared to Single Shot Detection (SSD) algorithm. © 2020 IEEE
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author 2-s2.0-85098280266
spellingShingle 2-s2.0-85098280266
Object detection for autonomous vehicle with Lidar using deep learning
author_facet 2-s2.0-85098280266
author_sort 2-s2.0-85098280266
title Object detection for autonomous vehicle with Lidar using deep learning
title_short Object detection for autonomous vehicle with Lidar using deep learning
title_full Object detection for autonomous vehicle with Lidar using deep learning
title_fullStr Object detection for autonomous vehicle with Lidar using deep learning
title_full_unstemmed Object detection for autonomous vehicle with Lidar using deep learning
title_sort Object detection for autonomous vehicle with Lidar using deep learning
publishDate 2020
container_title 2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/ICSET51301.2020.9265358
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098280266&doi=10.1109%2fICSET51301.2020.9265358&partnerID=40&md5=d7b18e9c18dadd6a6354bbf1ae3383bd
description This paper presents an object detection for Autonomous Vehicle (AV) using deep learning algorithm. Currently, most AVs use the camera for visualization to detect surrounding objects. However, the performance of a sensor, such as a camera with visual perception, is diminished in dim light, for instance at night-time due to the less light environment. Thus, the study attempts to employ the Light Detection and Ranging (LiDAR) sensor that uses light in the form of a pulsed laser to calculate ranges and ultimately detect objects. The use of LiDAR with the recent deep learning algorithm, namely You Only Look Once (YOLO) v2, was simulated on the Robot Operating System (ROS) in the Linux environment. The collected data has undergone several filtering processes, which includes noise removal, downsampling, and transformation. The study then applies the model on real-time data from the LiDAR sensor to perform object detection. The results show that YOLOv2 can identify the objects better compared to Single Shot Detection (SSD) algorithm. © 2020 IEEE
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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