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Testing ground-truth errors in an automotive dataset for a DNN-based object detector
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Li, Boda, Baris, Gabriele, Chan, Pak Hung, Rahman, Anima and Donzella, Valentina (2022) Testing ground-truth errors in an automotive dataset for a DNN-based object detector. In: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, 16-18 Nov 2022. Published in: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) ISBN 9781665470957. doi:10.1109/ICECCME55909.2022.9988623
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Official URL: https://doi.org/10.1109/ICECCME55909.2022.9988623
Abstract
Given the promising advances in the field of Assisted and Automated Driving, it is expected that the roads of the future will be populated by vehicles driven by computers, partially or fully replacing human drivers. In this scenario, the first stage of the perception-decision-actuation pipeline will likely rely on Deep Neural Networks for understanding the scene around the vehicle. Typical tasks for Deep Neural Networks are object detection and instance segmentation, tasks relying on supervised learning and annotated datasets. As one can imagine, the quality of the labelled dataset strongly affects the performance of the network, and this aspect is investigated in this paper. Annotation quality should be a primary concern in safety-critical tasks, such as Assisted and Automated Driving. This work addresses and classifies some of the mistakes found in a popular automotive dataset. Moreover, some experiments with a Deep Neural Network model were performed to test the effect of these mistakes on network predictions. A set of criteria was established to support the relabelling of the testing dataset which was compared to the original dataset.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TS Manufactures |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Intelligent transportation systems, Automated vehicles , User-centered system design, Neural networks (Computer science), Vehicular ad hoc networks (Computer networks), Automated guided vehicle systems, Driver assistance systems | |||||||||
| Journal or Publication Title: | 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) | |||||||||
| Publisher: | IEEE | |||||||||
| ISBN: | 9781665470957 | |||||||||
| Official Date: | 30 December 2022 | |||||||||
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| DOI: | 10.1109/ICECCME55909.2022.9988623 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 28 October 2022 | |||||||||
| Date of first compliant Open Access: | 31 October 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Maldives | |||||||||
| Date(s) of Event: | 16-18 Nov 2022 | |||||||||
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