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Leveraging Pre-Trained Detection Net...

University of Toronto (Canada).

Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets.
作者:	Walsh, Sean.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2020
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 82-06.
附註:	Advisor: Waslander, Steven.
Contained By:	Masters Abstracts International82-06.
標題:	Robotics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28025398
ISBN:	9798698545941

Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets.
Walsh, Sean.

Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 57 p.

Source: Masters Abstracts International, Volume: 82-06.

Thesis (M.A.S.)--University of Toronto (Canada), 2020.

This item must not be sold to any third party vendors.

The development of 3D object detection solutions for autonomous driving requires labelled real-world datasets for training and evaluation, which are costly to produce. This thesis presents a 3D click labelling approach to individual scene labelling, in which annotators are provided a specially designed user interface to click on objects in the point cloud of each scene. The click labels are subsequently processed by a pre-trained network to generate a label for each click. The proposed annotation scheme demonstrates a 30x lower human annotation time. This thesis also presents a method for automatic labelling of static objects through the use of full drive sequences. In this work proposals from a pre-trained detection network, over the course of a drive sequence, are transformed into a global reference frame. Overlapping detections are processed to produce high recall label proposals for static vehicles in the sequence, eliminating the need for manual annotation.

ISBN: 9798698545941Subjects--Topical Terms:

181952
Robotics.
Subjects--Index Terms:

Pre-trained detection network

Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets.
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520 $a The development of 3D object detection solutions for autonomous driving requires labelled real-world datasets for training and evaluation, which are costly to produce. This thesis presents a 3D click labelling approach to individual scene labelling, in which annotators are provided a specially designed user interface to click on objects in the point cloud of each scene. The click labels are subsequently processed by a pre-trained network to generate a label for each click. The proposed annotation scheme demonstrates a 30x lower human annotation time. This thesis also presents a method for automatic labelling of static objects through the use of full drive sequences. In this work proposals from a pre-trained detection network, over the course of a drive sequence, are transformed into a global reference frame. Overlapping detections are processed to produce high recall label proposals for static vehicles in the sequence, eliminating the need for manual annotation.
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