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RGB-D fusion models for construction and demolition waste detection by Jiantao Li; Huaiying Fang; Lulu Fan; Jianhong Yang; Tianchen Ji; Qiang Chen is a Environmental Science article available to read on EtoBox.

What is RGB-D fusion models for construction and demolition waste detection about?

The development of urbanization has brought a large amount of construction and demolition waste (CDW), which occupy land and cause adverse ecological effects. To effectively solve the negative impact of CDW, it needs to be recycled. Accurate waste classification is key to successful waste management. However, the current waste classification methods mainly use color images to classify, which cannot meet the needs of accurate classification. This paper built an RGB-depth (RGB-D) detection platform, using a color camera and a laser line-scanning sensor to collect RGB images and depth images. In order to use RGB images and depth images for feature fusion more effectively, this paper proposed three fusion models: RGB-D concat、RGB-D Ci-add and RGB-D Ci-concat. All these models based on an instance segmentation network called mask region convolutional neural network (Mask R-CNN), which can accurately segment the contours of each object while classifying them. The experimental results show that the mAPs of the RGB-D Ci-add / concat model are 1.33% to 1.72% higher than those of the RGB model, and the classification accuracy is 1.92% ∼ 2.27% higher. In addition, all the proposed models can

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Author

Jiantao Li; Huaiying Fang; Lulu Fan; Jianhong Yang; Tianchen Ji; Qiang Chen

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)