Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer

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Abstract

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10-4, 1.8 × 10-4, 0.8 × 10-4, and 1.0 × 10-4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management.

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A Survey on Transfer Learning

Sinno Jialin Pan, Shi Qiang Yang (2010)

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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin, Holger R. Roth, Mingchen Gao … (2016)

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

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A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Alom, Tarek M. Taha, Chris Yakopcic … (2019)

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

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Author and article information

Journal

Title: Journal of Environmental Management

Abbreviated Title: Journal of Environmental Management

Publisher: Elsevier BV

ISSN (Print): 03014797

Publication date Created: September 2022

Publication date (Print): September 2022

Volume: 318

Page: 115501

Article

DOI: 10.1016/j.jenvman.2022.115501

PubMed ID: 35717691

SO-VID: 2615f500-6c39-4eb6-9e1f-e173d739b2c2

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https://doi.org/10.15223/policy-037

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Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer

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Novel Coronavirus Disease COVID-19

Most cited references 33

A Survey on Transfer Learning

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

A State-of-the-Art Survey on Deep Learning Theory and Architectures

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