Towards quantifying the amount of uncollected garbage through image analysis

Susheel Suresh, Tarun Sharma, K. PrashanthT., V. Subramaniam, D. Sitaram, M. Nirupama
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引用次数: 5

Abstract

Civic authorities in many Indian cities have a tough time in garbage collection and as a result there is a pile up of garbage in the cities. In order to manage the situation, it is first required to be able to quantify the issue. In this paper, we address the problem of quantification of garbage in a dump using a two step approach. In the first step, we build a mobile application that allows citizens to capture images of garbage and upload them to a server. In the second step, back-end performs analysis on these images to estimate the amount of garbage using computer vision techniques. Our approach to volume estimation uses multiple images of the same dump (provided by the mobile application) from different perspectives, segments the dump from the background, reconstructs a three dimensional view of the dump and then estimates its volume. Using our novel pipeline, our experiments indicate that with 8 different perspectives, we are able to achieve an accuracy of about 85 % for estimating the volume.
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通过图像分析来量化未收集垃圾的数量
印度许多城市的市政当局在垃圾收集方面遇到了困难,因此城市里有一堆垃圾。为了管理这种情况,首先需要能够量化问题。在本文中,我们使用两步方法解决了垃圾场垃圾的量化问题。在第一步中,我们构建一个移动应用程序,允许公民捕获垃圾图像并将其上传到服务器。在第二步中,后端使用计算机视觉技术对这些图像进行分析以估计垃圾的数量。我们的体积估计方法是从不同的角度使用同一转储的多个图像(由移动应用程序提供),从背景中分割转储,重建转储的三维视图,然后估计其体积。使用我们的新管道,我们的实验表明,在8个不同的角度下,我们能够达到约85%的准确度来估计体积。
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