Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Nahyeong | - |
dc.contributor.author | An, Jhonghyun | - |
dc.date.accessioned | 2024-02-12T00:30:38Z | - |
dc.date.available | 2024-02-12T00:30:38Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90383 | - |
dc.description.abstract | In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments | - |
dc.type | Article | - |
dc.identifier.wosid | 001140675300001 | - |
dc.identifier.doi | 10.3390/s24010079 | - |
dc.identifier.bibliographicCitation | SENSORS, v.24, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85181928685 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 24 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | knowledge distillation | - |
dc.subject.keywordAuthor | off-road | - |
dc.subject.keywordAuthor | LiDAR point cloud | - |
dc.subject.keywordAuthor | self-driving | - |
dc.subject.keywordAuthor | point cloud projection | - |
dc.subject.keywordAuthor | range image | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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