A Holistic Vision-based Hazard Detection Framework for Asteroid Landings

Yan, Yunfeng; Qi, Donglian; Li, Chaoyong; Yu, Miao; Chen, Tuo

doi:10.1016/j.ifacol.2016.09.038

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gist of the deep learning methods proposed in [21][22][23][24][25] is their performance of the training feature compared to conservative classifier channels. Note that utilisation of big data sets for training requires models with a huge volume.…”

Section: Learning Nodesmentioning

confidence: 99%

Vision‐based crater and rock detection using a cascade decision forest

Yan

2019

IET Computer Vision

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Learning Nodesmentioning

confidence: 99%