Image Analysis Method of Construction Waste Filler Material
Components Based on Machine Vision

doi:10.12141/j.issn.1000-565X.200764

Journal of South China University of Technology (Natural Science Edition) ›› 2021, Vol. 49 ›› Issue (10): 50-58,69.doi: 10.12141/j.issn.1000-565X.200764

Special Issue: 2021年交通运输工程

• Traffic & Transportation Engineering • Previous Articles Next Articles

Image Analysis Method of Construction Waste Filler Material Components Based on Machine Vision

XIE Kang¹ CHEN Xiaobin¹ YAO Junkai² SU Qian³ CHEN Long¹ WU Mengli¹

1． School of Civil Engineering，Central South University，Changsha 410083，Hunan，China; 2． China Academy of Ｒailway Sciences，Beijing 100081，China． 3． School of Civil Engineering，Southwest Jiaotong University，Chengdu 610000，Sichuan，China

Received:2020-12-14 Revised:2021-04-25 Online:2021-10-25 Published:2021-09-30
Contact: 陈晓斌 ( 1978-) ，男，博士，教授，主要从事交通岩土工程研究。 E-mail:chen_xiaobin@csu.edu.cn
About author:谢康 ( 1995-) ，男，博士生，主要从事智能建设研究。E-mail:xiekang1995@csu.edu.cn
Supported by:
Supported by the National Natural Science Foundation of China ( 51978674)

Abstract

Abstract: Construction waste recycling filler is obtained from construction waste by crushing． Due to its diversified composition，It can be used for subgrade filling only after sorting． At present，manual screening method is timeconsuming． In this paper，convolution neural network was used for image analysis and the composition of regenerated packing can be obtained automatically． Firstly，a labeled dataset composed of 36000 granular images is created to train different CNN models． Among them，the user-defined resnet34 model with 40% Dropout rate performs the best，and its verification accuracy can reach 97% ． Secondly，the mass of particles was estimated based on the particle type and shape． Finally，the method proposed in this paper was compared with the manual screening method． For most recycled fillers，the quality difference is less than 2% ． This paper aims to improve the utilization of construction waste and it is of great significance to the popularization and application of construction waste backfill subgrade engineering．

Key words: construction waste, recycled filler, convolutional neuron network, deep learning, image identification

CLC Number:

U214. 01

XIE Kang, CHEN Xiaobin, YAO Junka, et al. Image Analysis Method of Construction Waste Filler Material Components Based on Machine Vision[J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(10): 50-58,69.

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Image Analysis Method of Construction Waste Filler Material Components Based on Machine Vision

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