What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

Fernando, Heshan; Marshall, Joshua A.

doi:10.1016/j.autcon.2020.103374

Cited by 22 publications

(10 citation statements)

References 30 publications

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“…The results of the proposed method on three base classifiers are shown in Tables 2 – 4 . The base classifiers include the SVM [ 28 ], K -nearest neighbor (KNN) [ 29 ], and random forest (RF) [ 30 ] methods. The recall of plastics in the FLF method and TLF method based on the SVM classifier is 99.2%, which is higher than that of bricks, concretes, foams, and woods.…”

Section: Resultsmentioning

confidence: 99%

A new method of construction waste classification based on two-level fusion

Lin

Zhao

et al. 2022

PLoS ONE

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The automatic sorting of construction waste (CW) is an essential procedure in the field of CW recycling due to its remarkable efficiency and safety. The classification of CW is the primary task that guides automatic and precise sorting. In our work, a new method of CW classification based on two-level fusion is proposed to promote classification performance. First, statistical histograms are used to obtain global hue information and local oriented gradients, which are called the hue histogram (HH) and histogram of oriented gradients (HOG), respectively. To fuse these visual features, a bag-of-visual-words (BoVW) method is applied to code HOG descriptors in a CW image as a vector, and this process is named B-HOG. Then, based on feature-level fusion, we define a new feature to combine HH and B-HOG, which represent the global and local visual characteristics of an object in a CW image. Furthermore, two base classifiers are used to learn the information from the color feature space and the new feature space. Based on decision-level fusion, we propose a joint decision-making model to combine the decisions from the two base classifiers for the final classification result. Finally, to verify the performance of the proposed method, we collect five types of CW images as the experimental data set and use these images to conduct experiments on three different base classifiers. Moreover, we compare this method with other extant methods. The results demonstrate that our method is effective and feasible.

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Section: Resultsmentioning

confidence: 99%

A new method of construction waste classification based on two-level fusion

Lin

Zhao

et al. 2022

PLoS ONE

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“…However, better results were achieved from one-class SVM (OC-SVM) and SVDD. Fernando and Marshall [13] performed a state of the art classification methodology for rock and gravel, by identifying force data (proprioceptive force data) acquired from load-haul-dump equipment with capacity of 14-tonne and adopting ANN, KNN, and k-mean algorithms. However, good results were obtained by ANN (5-NN) model with more realistic classification.…”

Section: Discussionmentioning

confidence: 99%

“…Supervised learning algorithms minimise the error between the targeted data and output data, whereas unsupervised algorithms are adopted for clustering data when data training is not preferable. However, both types of ML algorithms can be utilised for the material classifications based on the site conditions and circumstances for the availability of input data [13]. Construction material classification via ML techniques has gained a lot of attention among professionals and researchers in the construction sector.…”

Section: Introductionmentioning

confidence: 99%

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Alaloul¹,

Qureshi²

2021

Deep Learning Applications

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Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

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“…Bunrit [24]. Fernando and Marshal presented a classi cation methodology for excavation material identi cation utilizing only proprioceptive force data acquired from an autonomous digging system and three machine learning algorithms including KNN (K Nearest Neighbor), ANN, and k-means [25]. Davis et al designed and described a deep CNN to identify seven typical Construction and Demolition Waste (C&DW) classi cations using digital images of waste deposited in a construction site bin [26].…”

Section: Background and Related Workmentioning

confidence: 99%

Construction material classification on imbalanced datasets using Vision Transformer architecture (ViT)

Soleymani

Bonyani

Mahami

et al. 2022

Preprint

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This research proposes a reliable model for identifying different construction materials with the highest accuracy, which is exploited as an advantageous tool for a wide range of construction applications such as automated progress monitoring. In this study, a novel deep learning architecture called Vision Transformer (ViT) is used for detecting and classifying construction materials. The robustness of the proposed method is assessed by utilizing different image datasets. For this purpose, the model is trained and tested on two large imbalanced datasets, namely Construction Material Library (CML) and Building Material Dataset (BMD). A third dataset is also generated by combining CML and BMD to create a more imbalanced dataset and assess the capabilities of the proposed method. The achieved results reveal an accuracy of 100 percent in evaluation metrics such as accuracy, precision, recall, and f1-score for each material category of three different datasets. It is believed that the suggested model accomplishes a novel and robust tool for detecting and classifying different material types. To date, a number of studies have attempted to automatically classify a variety of building materials, which still have some errors. This research will address the mentioned shortcoming and proposes a model to detect the material type with higher accuracy. The proposed model is also capable of being generalized to different datasets.

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What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

Cited by 22 publications

References 30 publications

A new method of construction waste classification based on two-level fusion

A new method of construction waste classification based on two-level fusion

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Construction material classification on imbalanced datasets using Vision Transformer architecture (ViT)

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