MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection
<p dir="ltr">Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. However, they lack practicality for real-time pedestrian lane detecti...
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2023
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| _version_ | 1864513507679010816 |
|---|---|
| author | Sui Paul Ang (18460605) |
| author2 | Son Lam Phung (18460602) Soan T. M. Duong (18460599) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author author |
| author_facet | Sui Paul Ang (18460605) Son Lam Phung (18460602) Soan T. M. Duong (18460599) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sui Paul Ang (18460605) Son Lam Phung (18460602) Soan T. M. Duong (18460599) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2023-08-11T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10489-023-04682-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MSD-NAS_multi-scale_dense_neural_architecture_search_for_real-time_pedestrian_lane_detection/26788183 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Pedestrian lane detection Real-time video processing Neural architecture search Assistive navigation Deep learning Semantic segmentation |
| dc.title.none.fl_str_mv | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. However, they lack practicality for real-time pedestrian lane detection due to non-optimal accuracy, speed, and model size trade-off. Hence, an optimized deep neural network (DNN) for pedestrian lane detection is required. Designing a DNN from scratch is a laborious task that requires significant experience and time. This paper proposes a novel neural architecture search (NAS) algorithm, named MSD-NAS, to automate this laborious task. The proposed method designs an optimized deep network with multi-scale input branches, allowing the derived network to utilize local and global contexts for predictions. The search is also performed in a large and generic space that includes many existing hand-designed network architectures as candidates. To further boost performance, we propose a Short-term Visual Memory mechanism to improve information facilitation within the derived networks. Evaluated on the PLVP3 dataset of 10,000 images, the DNN designed by MSD-NAS achieves state-of-the-art accuracy (0.9781) and mIoU (0.9542), while being 20.16 times faster and 2.56 times smaller than the current best deep learning model.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-023-04682-6" target="_blank">https://dx.doi.org/10.1007/s10489-023-04682-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0718f31f3281bd57cb611769082a36cc |
| identifier_str_mv | 10.1007/s10489-023-04682-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26788183 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detectionSui Paul Ang (18460605)Son Lam Phung (18460602)Soan T. M. Duong (18460599)Abdesselam Bouzerdoum (17900021)Information and computing sciencesArtificial intelligenceMachine learningPedestrian lane detectionReal-time video processingNeural architecture searchAssistive navigationDeep learningSemantic segmentation<p dir="ltr">Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. However, they lack practicality for real-time pedestrian lane detection due to non-optimal accuracy, speed, and model size trade-off. Hence, an optimized deep neural network (DNN) for pedestrian lane detection is required. Designing a DNN from scratch is a laborious task that requires significant experience and time. This paper proposes a novel neural architecture search (NAS) algorithm, named MSD-NAS, to automate this laborious task. The proposed method designs an optimized deep network with multi-scale input branches, allowing the derived network to utilize local and global contexts for predictions. The search is also performed in a large and generic space that includes many existing hand-designed network architectures as candidates. To further boost performance, we propose a Short-term Visual Memory mechanism to improve information facilitation within the derived networks. Evaluated on the PLVP3 dataset of 10,000 images, the DNN designed by MSD-NAS achieves state-of-the-art accuracy (0.9781) and mIoU (0.9542), while being 20.16 times faster and 2.56 times smaller than the current best deep learning model.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-023-04682-6" target="_blank">https://dx.doi.org/10.1007/s10489-023-04682-6</a></p>2023-08-11T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10489-023-04682-6https://figshare.com/articles/journal_contribution/MSD-NAS_multi-scale_dense_neural_architecture_search_for_real-time_pedestrian_lane_detection/26788183CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267881832023-08-11T06:00:00Z |
| spellingShingle | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection Sui Paul Ang (18460605) Information and computing sciences Artificial intelligence Machine learning Pedestrian lane detection Real-time video processing Neural architecture search Assistive navigation Deep learning Semantic segmentation |
| status_str | publishedVersion |
| title | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| title_full | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| title_fullStr | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| title_full_unstemmed | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| title_short | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| title_sort | MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection |
| topic | Information and computing sciences Artificial intelligence Machine learning Pedestrian lane detection Real-time video processing Neural architecture search Assistive navigation Deep learning Semantic segmentation |