TY - GEN
T1 - MIXED RECYCLED AGGREGATES CLASSIFICATION USING IMAGING AND DEEP-LEARNING TECHNIQUES FOR EFFECTIVE WASTE MANAGEMENT IN REHABILITATION WORKS
AU - Jon Ander, Iturrioz Aguirre
AU - Verónica, García Cortes
AU - Artzai, Picón Ruiz
AU - Aitor, Alvarez Gila
AU - Jose Antonio, Arteche Vicario
N1 - Publisher Copyright:
© 2024, University of Cantabria - Building Technology R&D Group. All rights reserved.
PY - 2024
Y1 - 2024
N2 - The rehabilitation and renovation of existing buildings generate significant amounts of construction and demolition waste (CDW) [1]. Proper classification of this waste is crucial for effective waste management and resource recovery. However, identifying and classifying the waste can be a tedious, costly, and error-prone process, which can lead to inadequate treatment and have a significant impacton the environment. This study focuses on the development and laboratory testing of advanced classification methods based on image processing and semantic segmentation to classify mixed recycled aggregate fractions. A comprehensive comparison is made between two different approaches: a semantic segmentation without ground truth, i.e. training the neural network without a reference target, and ase mantic segmentation trained after exhaustive labeling of the classes. A dataset of images of different types of CDW, including pure samples of concrete, ceramics, and plaster, as well as prepared mixtures, was collected, labeled, and segmented. The study applies deeplearning techniques and evaluates the performance of the two methods by quantifying the amount ofeach component in the image. The first approach involves training a supervised semantic segmentation network to learn the distinctive features of each pure class of material without mixing and assigning semantic labels to pixels. The second approach involves manual labeling of the classes in mixed samplesas ground truth for the model. The results reveal that both approaches have distinct advantages and disadvantages. The ground truth- based approach provides an accurate and reliable reference but requires considerable effort in manual labeling. On the other hand, the classifier training approach is more efficient in terms of time and resources but may be subject to classification errors. We further show that fine-tunning the ground truth-free model on few labeled samples outperforms both alternatives and represents a data-efficient trade-off. In conclusion, this study demonstrates the potential of deep-learning techniques with image analysis for cost-effective CDW classification. The results obtained in this research can serve as a basis for developing more accurate and reliable methods for CDW identification, contributing to sustainable CDW management for rehabilitation and renovation works.
AB - The rehabilitation and renovation of existing buildings generate significant amounts of construction and demolition waste (CDW) [1]. Proper classification of this waste is crucial for effective waste management and resource recovery. However, identifying and classifying the waste can be a tedious, costly, and error-prone process, which can lead to inadequate treatment and have a significant impacton the environment. This study focuses on the development and laboratory testing of advanced classification methods based on image processing and semantic segmentation to classify mixed recycled aggregate fractions. A comprehensive comparison is made between two different approaches: a semantic segmentation without ground truth, i.e. training the neural network without a reference target, and ase mantic segmentation trained after exhaustive labeling of the classes. A dataset of images of different types of CDW, including pure samples of concrete, ceramics, and plaster, as well as prepared mixtures, was collected, labeled, and segmented. The study applies deeplearning techniques and evaluates the performance of the two methods by quantifying the amount ofeach component in the image. The first approach involves training a supervised semantic segmentation network to learn the distinctive features of each pure class of material without mixing and assigning semantic labels to pixels. The second approach involves manual labeling of the classes in mixed samplesas ground truth for the model. The results reveal that both approaches have distinct advantages and disadvantages. The ground truth- based approach provides an accurate and reliable reference but requires considerable effort in manual labeling. On the other hand, the classifier training approach is more efficient in terms of time and resources but may be subject to classification errors. We further show that fine-tunning the ground truth-free model on few labeled samples outperforms both alternatives and represents a data-efficient trade-off. In conclusion, this study demonstrates the potential of deep-learning techniques with image analysis for cost-effective CDW classification. The results obtained in this research can serve as a basis for developing more accurate and reliable methods for CDW identification, contributing to sustainable CDW management for rehabilitation and renovation works.
KW - CDW management
KW - Deep-learning techniques
KW - Image-based classification
KW - Mixed recycled aggregate fractions
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85202604436&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85202604436
SN - 9788409589906
T3 - REHABEND
SP - 1270
EP - 1277
BT - REHABEND 2024 - Construction Pathology, Rehabilitation Technology and Heritage Management
A2 - Boffill, Yosbel
A2 - Lombillo, Ignacio
A2 - Blanco, Haydee
PB - University of Cantabria - Building Technology R&D Group
T2 - 10th Euro-American Congress on Construction Pathology, Rehabilitation Technology and Heritage Management, REHABEND 2024
Y2 - 7 May 2024 through 10 May 2024
ER -