Application of intelligent image processing in the construction material industry

Abstract

This paper discusses two analysis activities in the construction material industry, which could be solved by intelligent image processing algorithms. The first task is the optical identification of recycled aggregates of construction and demolition waste (CDW) as basis of an innovative sorting method on the field of processing of CDW. The second and far more complicated task due to very high phenotypical object variabilities within the subclasses is the optical analysis of samples from mineral aggregates. The application of automatically optical identification methods can save time and thus costs. This is an important factor for small and medium-sized companies in the construction material industry.

Patterns and trends in Internet of Things (IoT) research: future applications in the construction industry

“…Image processing was one of the important clusters in the IoT research. Intelligent image processing algorithms of construction materials for quality control and inventory management is a primary focus of researchers (Anding et al, 2013) Lastly, an enquiry into the collaborative networks of countries is undertaken to visualise the major collaborations and leading nations along this field of enquiry. The minimum number of documents was set as five which resulted in 17 countries qualifying out of a total of 47 countries.…”

“…Cluster (4): image processing Image processing was one of the important clusters in the IoT research study. Intelligent image processing algorithms of construction materials for quality control and inventory management are a primary focus of researchers (Anding et al, 2013). Themes like camera, equipment, excavator, image, operator, part, time, vehicle and vision have been the focus of research conducted.…”

Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

“…It consists of a large number of decision trees, a so-called decision forest, and thus belongs to the ensemble methods 11 . This classifier achieves very good results in a short time, even on complex, highly nonlinear recognition tasks 12,13 . Furthermore, it has only a few parameters to be set.…”

Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation

“…A major current focus in this context has been the identification and classification of the materials C&D is composed of, with the aim of producing recycled 'green' concrete. Da Fonseca Martins Gomes et al (2010) for instance developed a machine vision system based on shape features for detecting mortar, ceramic and concrete within C&D. Gokyuu et al (2011) used colour features and Bayesian classification to sort byproducts of different size and shape from C&D. Likewise, Anding et al (2013) proposed a computer vision system based on colour imaging and support vector classification for differentiating phenotypically similar materials such as concrete, aerated concrete, lightweight concrete, porous brick and dense brick. More recently, Palmieri et al (2014) described a system based on hyperspectral imaging for detecting unwanted contaminants like brick, gypsum, wood and plastics in C&D.…”