College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, China.
Shenzhen Municipal Engineering Corporation, Shenzhen, China.
Waste Manag. 2022 Feb 15;139:96-104. doi: 10.1016/j.wasman.2021.12.021. Epub 2021 Dec 23.
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 meet the real-time requirement of online detection. Due to the excellent comprehensive performance of the RGB-D Ci-concat model, it can be regarded as the final detection model of the robot, which can improve the sorting efficiency of CDW further.
城市化的发展带来了大量的建筑和拆除垃圾（CDW），这些垃圾占用土地并造成不良的生态影响。为了有效解决 CDW 的负面影响，需要对其进行回收利用。准确的废物分类是成功废物管理的关键。然而，目前的废物分类方法主要使用彩色图像进行分类，无法满足准确分类的需求。
本文构建了一个 RGB-深度（RGB-D）检测平台，使用彩色相机和激光线扫描传感器采集 RGB 图像和深度图像。为了更有效地利用 RGB 图像和深度图像进行特征融合，本文提出了三种融合模型：RGB-D concat、RGB-D Ci-add 和 RGB-D Ci-concat。所有这些模型都基于一个实例分割网络，称为掩模区域卷积神经网络（Mask R-CNN），它可以在对物体进行分类的同时准确地分割出每个物体的轮廓。实验结果表明，RGB-D Ci-add/concat 模型的 mAPs 比 RGB 模型高 1.33%至 1.72%，分类准确率高 1.92%至 2.27%。此外，所有提出的模型都可以满足在线检测的实时要求。由于 RGB-D Ci-concat 模型具有出色的综合性能，因此可以将其视为机器人的最终检测模型，从而进一步提高 CDW 的分拣效率。
