Construction consumption measurement method based on convolutional cloud image recognition technique

Lili Zhou; Yong Liu

doi:10.1117/12.3050753

24 October 2024 Construction consumption measurement method based on convolutional cloud image recognition technique

Lili Zhou, Yong Liu

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 1339606 (2024) https://doi.org/10.1117/12.3050753
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

This study aims to explore the construction consumption determination method based on convolutional cloud neural network image recognition technology. By analyzing the application of convolutional cloud neural network in the field of image recognition and combining the actual situation and data collection at the construction site, a construction consumption determination method based on convolutional cloud neural network is proposed. By processing and analyzing the image data of the construction site, combined with the deep learning algorithm, accurate determination of the consumption of various materials and resources in the construction process is realized. The results show that the construction consumption measurement method based on convolutional cloud neural network image recognition technology has high accuracy and practicability, and can provide effective support for construction management and cost control.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lili Zhou and Yong Liu "Construction consumption measurement method based on convolutional cloud image recognition technique", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 1339606 (24 October 2024); https://doi.org/10.1117/12.3050753

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KEYWORDS

Clouds

Neural networks

Education and training

Data modeling

Image processing

Video surveillance

Feature extraction

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