基于激光雷达的海滩垃圾快速识别

何钰滢; 葛振鹏; 李道季; 施华宏; 韩震; 戴志军

doi:10.3969/j.issn.0253-4193.2019.11.015

基于激光雷达的海滩垃圾快速识别

doi: 10.3969/j.issn.0253-4193.2019.11.015

1.
华东师范大学河口海岸学国家重点实验室，上海 200241
2.
上海海洋大学海洋科学学院，上海 201306

基金项目: 国家重点研发项目（2016YFC1402202）；国家自然科学基金（41576087）。

详细信息

作者简介:
何钰滢（1994—），女，浙江省绍兴市人，从事河口海岸动力地貌及环境监测研究。E-mail：heyuying9453@163.com

通讯作者:
戴志军，教授，博士生导师，主要从事河口海岸动力地貌模拟及模式预测。E-mail：zjdai@sklec.ecnu.edu.cn

中图分类号: TN959.72
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- 被引次数: 0
出版历程
- 收稿日期: 2018-09-22
- 修回日期: 2018-12-14
- 网络出版日期: 2021-04-21
- 刊出日期: 2019-11-25

LiDAR-based quickly recognition of beach debris

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

摘要

摘要: 全球日益增多的海滩垃圾，不仅造成海洋环境污染，严重威胁海洋生态系统健康，也对生物栖息地有着不可估计的影响。如何高效准确地对海滩垃圾进行监测和识别，是处置海滩垃圾过程的技术难点之一。基于此，本文以长江口南汇边滩为实验区，通过在海滩上设置常见垃圾样品，随后利用激光雷达记录的全波形数据和BP神经网络模型，以快速鉴别海滩垃圾类型。结果表明：基于激光雷达提取的垃圾全波形数据中回波振幅和回波宽度的差异，可用来识别海滩垃圾。构建的BP神经网络可有效将海滩垃圾分为泡沫类、布类、金属类、纸类及塑料类，最高识别率达到79%。此外，由于不同材质海滩垃圾的原材料成分存在相似或同质，会对精确识别区分垃圾类型造成一定的干扰，从而影响神经网络的识别率。可见，将激光雷达应用于识别海滩垃圾，为海滩垃圾的监测提供了新的方法。
- 激光雷达 /
- BP神经网络 /
- 海滩垃圾识别
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

HTML全文

图 1 南汇边滩的实验区域

a.实验全景图；b.Riegl VZ-4000扫描仪

Fig. 1 Experimental area of Nanhui Beach

a. Photo of the experiment; b. Riegl VZ-4000 terrestrial laser scanner

下载: 全尺寸图片幻灯片

图 2 不同类型的海滩垃圾

a. 原实验中不同类型的海滩垃圾样品；b. 野外验证实验中不同类型的海滩垃圾样品

Fig. 2 The typical beach debris

a. The typical beach debris in the first experiment; b. the typical beach debris in the verification experiment

下载: 全尺寸图片幻灯片

图 3 过滤后获取的疑似垃圾点

Fig. 3 The debris point-cloud after filtering

下载: 全尺寸图片幻灯片

图 4 海滩垃圾的校正回波振幅的频率分布直方图

Fig. 4 Frequency histogram of the corrected amplitude of the beach debris

下载: 全尺寸图片幻灯片

图 5 海滩垃圾的校正回波宽度的频率分布直方图

Fig. 5 Frequency histogram of the corrected width of the beach debris

下载: 全尺寸图片幻灯片

图 6 海滩垃圾测试集的识别结果

a. 原实验的识别结果；b. 野外验证实验的识别结果；1.泡沫类，2.布类，3.金属类，4.纸类，5.塑料类

Fig. 6 Recognition results of debris point-cloud

a. Recognition results of the first experiment; b. recognition results of the verification experiment ；1.foam，2.cloth，3.metal；4.paper，5.plastic

下载: 全尺寸图片幻灯片

图 7 海滩垃圾的回波振幅与波宽的分布

Fig. 7 The distribution between the corrected amplitude and the corrected width

下载: 全尺寸图片幻灯片

参考文献(21)

[1]	Li W C, Tse H F, Fok L. Plastic waste in the marine environment: a review of sources, occurrence and effects[J]. Science of the Total Environment, 2016, 566–567: 333−349. doi: 10.1016/j.scitotenv.2016.05.084
[2]	赵肖, 綦世斌, 廖岩, 等. 我国海滩垃圾污染现状及控制对策[J]. 环境科学研究, 2016, 29(10): 1560−1566. Zhao Xiao, Qi Shibin, Liao Yan, et al. Investigation and control of beach litter pollution in China[J]. Research of Environmental Sciences, 2016, 29(10): 1560−1566.
[3]	UNEP. Marine litter, an analytical overview[Z]. UNEP Marine Litter Publications, 2005.
[4]	Villarrubia-Gómez P, Cornell S E, Fabres J. Marine plastic pollution as a planetary boundary threat–The drifting piece in the sustainability puzzle[J]. Marine Policy, 2018, 96: 213−220. doi: 10.1016/j.marpol.2017.11.035
[5]	Kako S, Isobe A, Magome S. Sequential monitoring of beach litter using webcams[J]. Marine Pollution Bulletin, 2010, 60(5): 775−779. doi: 10.1016/j.marpolbul.2010.03.009
[6]	Kako S, Isobe A, Magome S. Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera[J]. Marine Pollution Bulletin, 2012, 64(6): 1156−1162. doi: 10.1016/j.marpolbul.2012.03.024
[7]	Kataoka T, Hinata H, Kako S. A new technique for detecting colored macro plastic debris on beaches using webcam images and CIELUV[J]. Marine Pollution Bulletin, 2012, 64(9): 1829−1836. doi: 10.1016/j.marpolbul.2012.06.006
[8]	侯峰. LIDAR详细介绍及其应用举例综述[J]. 科技广场, 2014(4): 95−100. doi: 10.3969/j.issn.1671-4792.2014.04.020 Hou Feng. Thorough introduction of LIDAR and overview of its application[J]. Science Mosaic, 2014(4): 95−100. doi: 10.3969/j.issn.1671-4792.2014.04.020
[9]	王金虎, 李传荣, 周梅. 全波形激光雷达数据在点云分类中的应用研究[J]. 遥感信息, 2013, 28(5): 21−27. doi: 10.3969/j.issn.1000-3177.2013.05.005 Wang Jinhu, Li Chuanrong, Zhou Mei. Analysis of airborne full-waveform LiDAR data for supervised point cloud classification[J]. Remote Sensing Information, 2013, 28(5): 21−27. doi: 10.3969/j.issn.1000-3177.2013.05.005
[10]	Reitberger J, Krzystek P, Stilla U. Analysis of full waveform LiDAR data for the classification of deciduous and coniferous trees[J]. International Journal of Remote Sensing, 2008, 29(5): 1407−1431. doi: 10.1080/01431160701736448
[11]	Höfle B. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 94−98. doi: 10.1109/LGRS.2013.2247022
[12]	冯义从, 岑敏仪, 张同刚. 基于知识的车载LiDAR地物自动分类[J]. 计算机工程与应用, 2016, 52(5): 122−126. doi: 10.3778/j.issn.1002-8331.1403-0361 Feng Yicong, Cen Minyi, Zhang Tonggang. Knowledge-based automatic objects classification from mobile LiDAR data[J]. Computer Engineering and Applications, 2016, 52(5): 122−126. doi: 10.3778/j.issn.1002-8331.1403-0361
[13]	Ge Zhenpeng, Shi Huahong, Mei Xuefei, et al. Semi-automatic recognition of marine debris on beaches[J]. Scientific Reports, 2016, 6: 25759. doi: 10.1038/srep25759
[14]	赵建春, 李九发, 李占海, 等. 长江口南汇嘴潮滩短期冲淤演变及其动力机制研究[J]. 海洋学报, 2009, 31(4): 103−111. doi: 10.3321/j.issn:0253-4193.2009.04.012 Zhao Jianchun, Li Jiufa, Li Zhanhai, et al. Researches on characteristics and dynamic mechanism of short-term scouring and silting changes of the tidal flat on Nanhui Spit in the Changjiang Estuary in China[J]. Haiyang Xuebao, 2009, 31(4): 103−111. doi: 10.3321/j.issn:0253-4193.2009.04.012
[15]	戴志军, 陈吉余, 程和琴, 等. 南汇边滩的沉积特征和沉积物输运趋势[J]. 长江流域资源与环境, 2005, 14(6): 735−739. doi: 10.3969/j.issn.1004-8227.2005.06.013 Dai Zhijun, Chen Jiyu, Cheng Heqin, et al. Sediment characteristics and transport patterns in Nanhui Joint Area[J]. Resources and Environment in the Yangtze Basin, 2005, 14(6): 735−739. doi: 10.3969/j.issn.1004-8227.2005.06.013
[16]	Persson Å, Söderman U, Töpel J, et al. Visualization and analysis of full-waveform airborne laser scanner data[R]//ISPRS WG Ⅲ/3, Ⅲ/4, V/3 Workshop "Laser Scanning 2005". Enschede, The Netherlands, 2005: 103–108.
[17]	Wagner W, Ullrich A, Ducic V, et al. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2006, 60(2): 100−112. doi: 10.1016/j.isprsjprs.2005.12.001
[18]	Wagner W. Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: basic physical concepts[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(6): 505−513. doi: 10.1016/j.isprsjprs.2010.06.007
[19]	杨斐, 王坤明, 马欣, 等. 应用BP神经网络分类器识别交通标志[J]. 计算机工程, 2003, 29(10): 120−121. doi: 10.3969/j.issn.1000-3428.2003.10.049 Yang Fei, Wang Kunming, Ma Xin, et al. Application of BP neural network classifier for road traffic sign recognition[J]. Computer Engineering, 2003, 29(10): 120−121. doi: 10.3969/j.issn.1000-3428.2003.10.049
[20]	裴洪平, 罗妮娜, 蒋勇. 利用BP神经网络方法预测西湖叶绿素a的浓度[J]. 生态学报, 2004, 24(2): 246−251. doi: 10.3321/j.issn:1000-0933.2004.02.012 Pei Hongping, Luo Nina, Jiang Yong. Applications of back propagation neural network for predicting the concentration of chlorophyll-a in West Lake[J]. Acta Ecologica Sinica, 2004, 24(2): 246−251. doi: 10.3321/j.issn:1000-0933.2004.02.012
[21]	刘峰, 于九皋. 淀粉在泡沫塑料制品中的应用[J]. 化学工业与工程, 2000, 17(1): 43−48. doi: 10.3969/j.issn.1004-9533.2000.01.009 Liu Feng, Yu Jiugao. Appliance of starch in foamed plastic products[J]. Chemical Industry and Engineering, 2000, 17(1): 43−48. doi: 10.3969/j.issn.1004-9533.2000.01.009