Vision‐based crater and rock detection using a cascade decision forest
Both crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approache...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5600 |
| _version_ | 1827817403117993984 |
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| author | Yunfeng Yan Donglian Qi Chaoyong Li |
| author_facet | Yunfeng Yan Donglian Qi Chaoyong Li |
| author_sort | Yunfeng Yan |
| collection | DOAJ |
| description | Both crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approaches, which potentially use the pattern recognition techniques of ring threshold finding, perform quickly; however, they suffer from handling small craters. The supervised pattern recognition method is more powerful but is time‐consuming. To address these issues, here, a simultaneous multi‐size crater and rock detection algorithm is studied. The authors propose a new supervised machine‐learning framework using a cascade decision forest. Sliding windows are utilised in order to search basic features, and a multi‐grained cascade structure is introduced to enhance the framework's ability to learn the representations of the features. The training time of the proposed algorithm on a PC is comparable to that of deep neural networks, and the efficiency is enhanced for a large‐scale database. The outputs of the simulation verify the effectiveness and validity of the introduced technique. |
| first_indexed | 2024-03-12T00:33:46Z |
| format | Article |
| id | doaj.art-06439bfa54634ea8839ea45979be7a79 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:33:46Z |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-06439bfa54634ea8839ea45979be7a792023-09-15T10:01:28ZengWileyIET Computer Vision1751-96321751-96402019-09-0113654955510.1049/iet-cvi.2018.5600Vision‐based crater and rock detection using a cascade decision forestYunfeng Yan0Donglian Qi1Chaoyong Li2College of Electrical Engineering, Zhejiang UniversityHangzhouPeople's Republic of ChinaCollege of Electrical Engineering, Zhejiang UniversityHangzhouPeople's Republic of ChinaCollege of Electrical Engineering, Zhejiang UniversityHangzhouPeople's Republic of ChinaBoth crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approaches, which potentially use the pattern recognition techniques of ring threshold finding, perform quickly; however, they suffer from handling small craters. The supervised pattern recognition method is more powerful but is time‐consuming. To address these issues, here, a simultaneous multi‐size crater and rock detection algorithm is studied. The authors propose a new supervised machine‐learning framework using a cascade decision forest. Sliding windows are utilised in order to search basic features, and a multi‐grained cascade structure is introduced to enhance the framework's ability to learn the representations of the features. The training time of the proposed algorithm on a PC is comparable to that of deep neural networks, and the efficiency is enhanced for a large‐scale database. The outputs of the simulation verify the effectiveness and validity of the introduced technique.https://doi.org/10.1049/iet-cvi.2018.5600cascade decision forestcraterautonomous landingcratersrockslanding hazards |
| spellingShingle | Yunfeng Yan Donglian Qi Chaoyong Li Vision‐based crater and rock detection using a cascade decision forest IET Computer Vision cascade decision forest crater autonomous landing craters rocks landing hazards |
| title | Vision‐based crater and rock detection using a cascade decision forest |
| title_full | Vision‐based crater and rock detection using a cascade decision forest |
| title_fullStr | Vision‐based crater and rock detection using a cascade decision forest |
| title_full_unstemmed | Vision‐based crater and rock detection using a cascade decision forest |
| title_short | Vision‐based crater and rock detection using a cascade decision forest |
| title_sort | vision based crater and rock detection using a cascade decision forest |
| topic | cascade decision forest crater autonomous landing craters rocks landing hazards |
| url | https://doi.org/10.1049/iet-cvi.2018.5600 |
| work_keys_str_mv | AT yunfengyan visionbasedcraterandrockdetectionusingacascadedecisionforest AT donglianqi visionbasedcraterandrockdetectionusingacascadedecisionforest AT chaoyongli visionbasedcraterandrockdetectionusingacascadedecisionforest |