Martin Cecilia, Zhang Qiannan, Zhai Dongjun, Zhang Xiangliang, Duarte Carlos M
Red Sea Research Center and Computational Bioscience Research Center, King Abdullah University of Science and Technology, 23955, Thuwal, Saudi Arabia.
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, 23955, Thuwal, Saudi Arabia.
Environ Pollut. 2021 May 15;277:116730. doi: 10.1016/j.envpol.2021.116730. Epub 2021 Feb 13.
Beach litter assessments rely on time inefficient and high human cost protocols, mining the attainment of global beach litter estimates. Here we show the application of an emerging technique, the use of drones for acquisition of high-resolution beach images coupled with machine learning for their automatic processing, aimed at achieving the first national-scale beach litter survey completed by only one operator. The aerial survey had a time efficiency of 570 ± 40 m min and the machine learning reached a mean (±SE) detection sensitivity of 59 ± 3% with high resolution images. The resulting mean (±SE) litter density on Saudi Arabian shores of the Red Sea is of 0.12 ± 0.02 litter items m, distributed independently of the population density in the area around the sampling station. Instead, accumulation of litter depended on the exposure of the beach to the prevailing wind and litter composition differed between islands and the main shore, where recreational activities are the major source of anthropogenic debris.
海滩垃圾评估依赖于效率低下且人力成本高昂的方案，这影响了全球海滩垃圾估计数的获取。在此，我们展示了一种新兴技术的应用，即使用无人机获取高分辨率海滩图像，并结合机器学习进行自动处理，旨在实现仅由一名操作员完成的首次全国范围海滩垃圾调查。空中调查的时间效率为570±40米/分钟，机器学习对高分辨率图像的平均（±标准误）检测灵敏度达到59±3%。红海沙特阿拉伯海岸的垃圾平均（±标准误）密度为0.12±0.02件/平方米，其分布与采样站周围地区的人口密度无关。相反，垃圾的堆积取决于海滩对盛行风的暴露程度，岛屿和主要海岸的垃圾成分不同，在主要海岸，娱乐活动是人为垃圾的主要来源。
