nach oben

Journal of Material Cycles and Waste Management

20.04.2024 | ORIGINAL ARTICLE

Real-time detection of construction and demolition waste impurities using the improved YOLO-V7 network

verfasst von: Haifeng Fang, Junji Chen, Mingqiang Wang, Qunbiao Wu, Zhen Wang

Erschienen in: Journal of Material Cycles and Waste Management

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Construction and demolition waste accounts for a considerable part of the total waste flow of the city. The most common way to recycle it is to make it into recycled aggregate. In the process of recycling and preparing recycled aggregate from the construction and demolition waste, it is necessary to manually screen out impurities that remain after wind selection, water floating, etc. This not only increases production costs but also affects the quality of recycled aggregates and the utilization rate of construction and demolition waste. This study proposes an automated method for detecting construction and demolition waste using an improved object detection network. By improving the feature fusion layer, the convolutional block, and the loss function of the YOLOv7 object detection network, the recognition accuracy, the recall rate, and the mean average precision of the network have been greatly improved, while the number of parameters has been further reduced. Therefore, the improved YOLOV7 network can effectively identify various impurities in the dismantled waste, providing technical support for automatic detection and screening of construction and demolition waste impurities robots, improving the efficiency of enterprise processing of construction and demolition waste, and indirectly alleviating environmental problems and resource waste caused by construction and demolition waste.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Hoornweg D, Bhada-Tata P (2012) What a waste: a global review of solid waste management.

de Andrade Salgado F, de Andrade Silva F (2022) Recycled aggregates from construction and demolition waste towards an application on structural concrete: a review. J BuildEng 52:104452CrossRef

Redmon J, Divvala S, Girshick R, Farhadi A, (2016) You only look once: unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788.

Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271.

Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, arXiv preprint arXiv:1804.02767, 2018.

Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

ultralytics. yolov5. https://github.com/ultralytics/yolov5. Accessed on 18 May 2020.

Wang CY, Bochkovskiy A, Liao HYM (2022) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696.

Girshick R, Donahue J, Darrell T, Malik J (2015) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158CrossRef

10.

Girshick R (2015) Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448.

11.

Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149CrossRef

12.

Liu W, Anguelov D, Erhan D, Szegedy C (2016) SSD: single shot multibox detector. European Conference on Computer Vision, pp. 21–37.

13.

Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) Dssd: deconvolutional single shot detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, arXiv preprint arXiv:1701.06659.

14.

Lukka TJ, Tossavainen T, Kujala JV, Raiko T (2014) Zenrobotics recycler–robotic sorting using machine learning. Proceedings of the International Conference on Sensor-Based Sorting (SBS), 1–8.

15.

Kujala JV, Lukka TJ, Holopainen H (2015) Picking a conveyor clean by an autonomously learning robot, arXiv preprint arXiv:1511.07608.

16.

Xiao W, Yang J, Fang H, Zhuang J, Ku Y (2020) Classifying construction and demolition waste by combining spatial and spectral features. Proc Inst Civ Eng Waste Resour Manage 173(3):79–90

17.

Davis P, Aziz F, Newaz MT, Sher W, Simon L (2021) The classification of construction waste material using a deep convolutional neural network. Autom Constr 122:103481CrossRef

18.

Chen J, Lu W, Yuan L, Wu Y, Xue F (2022) Estimating construction waste truck payload volume using monocular vision. Resour Conserv Recycl 177:106013CrossRef

19.

[Yang, M., Thung, G., 2016. Classification of Trash for Recyclability Status. CS229 Project Report 2016.

20.

Majchrowska S, Mikołajczyk A, Ferlin M, Klawikowska Z, Plantykow MA, Kwasigroch A, Majek K (2022) Deep learning-based waste detection in natural and urban environments. Waste Manag 138:274–284CrossRef

21.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9.

22.

Lin TY, Dollár P, Girshick R, He KM, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125.

23.

Liu S, Qi L, Qin HF, Shi JP, Jia JY (2018). In: Path aggregation network for instance segmentation. Salt Lake City, America, pp. 8759–8768.

24.

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T., Andreetto M, Adam H (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

25.

Li H, Li J, Wei H, Liu Z, Zhan Z, Ren Q (2022) Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424.

26.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pp. 618–626.

Titel: Real-time detection of construction and demolition waste impurities using the improved YOLO-V7 network
verfasst von: Haifeng Fang
Junji Chen
Mingqiang Wang
Qunbiao Wu
Zhen Wang
Publikationsdatum: 20.04.2024
Verlag: Springer Japan
Erschienen in: Journal of Material Cycles and Waste Management
Print ISSN: 1438-4957
Elektronische ISSN: 1611-8227
DOI: https://doi.org/10.1007/s10163-024-01960-4