LiDAR-based quickly recognition of beach debris

He Yuying; Ge Zhenpeng; Li Daoji; Shi Huahong; Han Zhen; Dai Zhijun

doi:10.3969/j.issn.0253-4193.2019.11.015

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He Yuying，Ge Zhenpeng，Li Daoji, et al. LiDAR-based quickly recognition of beach debris[J]. Haiyang Xuebao，2019, 41(11)：156–162，doi:10.3969/j.issn.0253−4193.2019.11.015

Citation:

He Yuying，Ge Zhenpeng，Li Daoji, et al. LiDAR-based quickly recognition of beach debris[J]. Haiyang Xuebao，2019, 41(11)：156–162，doi:10.3969/ j.issn.0253−4193.2019.11.015

He Yuying，Ge Zhenpeng，Li Daoji, et al. LiDAR-based quickly recognition of beach debris[J]. Haiyang Xuebao，2019, 41(11)：156–162，doi:10.3969/j.issn.0253−4193.2019.11.015

Citation:

He Yuying，Ge Zhenpeng，Li Daoji, et al. LiDAR-based quickly recognition of beach debris[J]. Haiyang Xuebao，2019, 41(11)：156–162，doi:10.3969/ j.issn.0253−4193.2019.11.015

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LiDAR-based quickly recognition of beach debris

doi: 10.3969/j.issn.0253-4193.2019.11.015

1.
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2018-09-22
Rev Recd Date: 2018-12-14

Available Online: 2021-04-21

Publish Date: 2019-11-25

Abstract

Abstract

There is an increasing amount of beach debris worldwide which have serious impacts on the marine environment, especially to the marine ecosystem health and biological habitats. It has been one of the great technological difficulties on how to monitor and identify beach debris efficiently during the process of the accurately disposing beach debris. Therefore, in this paper, a new recognition method of beach debris was proposed based on field beach debris experiment on Nanhui Beach by combination of LiDAR (light detection and ranging) with record full waveform data and the Back Propagation (BP) neural network model. The results reveal that the echo amplitude and width extracted from full-waveform data can be used to identify beach debris because of their distinct waveform features. Meanwhile, beach debris can be effectively classified into foam, cloth, metal, paper and plastic with the highly accuracy rate of 79% by the BP neural network recognition. Moreover, it can be found that some beach debris are difficult to identify owing to the same material composition for these debris, which may disturb the recognition rate of BP neural network to great degree. Therefore, it can be expected that a new monitoring tool for beach debris identification by LiDAR will be popular in future.
- LiDAR,
- BP neural network,
- beach debris recognition

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References(21)

References

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