Graduate School of Science and Engineering, Department of Ocean Civil Engineering, Kagoshima University, Kagoshima, Japan.
Graduate School of Science and Engineering, Department of Ocean Civil Engineering, Kagoshima University, Kagoshima, Japan.
Mar Pollut Bull. 2020 Jun;155:111127. doi: 10.1016/j.marpolbul.2020.111127. Epub 2020 May 3.
Plastic marine debris (PMD) is of global concern. To help address this problem, a novel approach for estimating PMD volumes using a combination of unmanned aerial vehicle (UAV) surveys and image processing based on deep learning is proposed. A three-dimensional model and orthoscopic image of a beach, constructed via Structure from Motion software using UAV-derived data, enabled PMD volumes to be computed by edge detection through image processing. The accuracy of the method was verified by estimating the volumes of test debris placed on a beach in known sizes and shapes. The proposed approach shows potential for estimating PMD volumes with an error of <5%. Compared with subjective methods based on beach surveys, this approach can accurately, rapidly, and objectively calculate the PMD volume on a beach and can be used to improve the efficiency of beach surveys and identify beaches that need preferential cleaning.
塑料海洋垃圾（PMD）是全球性问题。为了解决这个问题，提出了一种使用无人机（UAV）调查和基于深度学习的图像处理相结合来估算 PMD 体积的新方法。通过使用无人机衍生数据的运动结构软件构建的海滩的三维模型和正射图像，通过图像处理进行边缘检测来计算 PMD 体积。通过估计放置在海滩上的已知大小和形状的测试碎片的体积来验证该方法的准确性。该方法具有估算 PMD 体积误差<5%的潜力。与基于海滩调查的主观方法相比，该方法可以准确、快速、客观地计算海滩上的 PMD 体积，并可用于提高海滩调查的效率和识别需要优先清理的海滩。
