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基于图像的城市场景垃圾自动检测

Image-Based Garbage Detection in Urban Scenes

  • 摘要: 基于城市场景照片快速准确地自动检测垃圾在“智慧城管”等应用中具有重要的研究价值。城市垃圾在颜色纹理、几何形态上具有极大的多样性, 甚至部分垃圾的认定具有一定的主观性, 这给垃圾自动检测带来很大的挑战。文章提出了一种基于高速区域卷积神经网络的垃圾检测方法, 通过使用数据融合、数据扩充、迁移学习等方法解决训练样本不足的问题, 实现了城市场景图片中垃圾的自动、快速、准确检测。文章最后基于深圳市道路垃圾照片构建了一个包含多种形态类型垃圾的垃圾图片数据库, 在该库中垃圾检测准确度高达 89.07%。

     

    Abstract: It is of great value to rapidly and accurately detect garbage from urban images in the application of intelligent city management. Garbage images are highly diverse in color texture and geometry; moreover, garbage recognition can be a subject matter, which poses great challenges to automatic detection of garbage. In this paper, a garbage detection method based on faster region-based convolutional neural networks was proposed. It can detect garbage from urban images with high accuracy by integrating techniques such as data fusion, data augmentation, and transfer learning. We have built an image database containing various types of garbage based on photographs taken from urban scenes in the Shenzhen city, showing a detection accuracy of 89.07%.

     

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