Detection and Application of Concrete Compressive Strength with Machine Vision Technology

Autor: Chen Yan, Zhou Hailong

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

This paper proposes research on concrete compressive strength detection based on machine vision technology in response to the problems of traditional concrete compressive strength detection methods. The original image is directly input into the netwo

Externí odkaz: https://doaj.org/article/377cdf5fa9e443fd9d62f7ffe82747ce

Aerial Identification of Amazonian Palms in High-Density Forest Using Deep Learning

Autor: Willintong Marin, Ivan F. Mondragon, Julian D. Colorado

Publikováno v: Forests, Vol 13, Iss 5, p 655 (2022)

This paper presents an integrated aerial system for the identification of Amazonian Moriche palm (Mauritia flexuosa) in dense forests, by analyzing the UAV-captured RGB imagery using a Mask R-CNN deep learning approach. The model was trained with 478

Externí odkaz: https://doaj.org/article/83604a7758b54249b4a6a1408fa061bb

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Techniques for the Automatic Detection and Hiding of Sensitive Targets in Emergency Mapping Based on Remote Sensing Data

Autor: Chao Wu, Tianqi Qiu, Pengjie Lu, Xiaojin Liang, Fu Ren, Qingyun Du

Publikováno v: ISPRS International Journal of Geo-Information, Vol 10, Iss 68, p 68 (2021)
ISPRS International Journal of Geo-Information
Volume 10
Issue 2

Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algori

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07ac36ff25bfd9198d5f697b59a418ae
https://www.mdpi.com/2220-9964/10/2/68

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