TY - GEN
T1 - Semantic Enhanced Segmentation Based on Thermal Images with Superpixel
AU - Xu, Y.
AU - Huang, H.
AU - Zhang, C.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Semantic segmentation is receiving increasing attention from researchers. Many emerging applications require accurate and efficient segmentation mechanisms, and this need coincides with the rise of almost all fields related to computer vision, especially in the construction industry. Building a semantic segmentation model for accurate and rich semantic information to identify, classify and segment building/structural components in construction site scenarios can greatly improve the productivity and informatization of the construction industry and has great potential for automated construction and visual monitoring. However, the accuracy of traditional image recognition technology is low, and it is difficult to adapt to the complex environment, and the illumination and Angle factors will affect the reliability of the final model. To address these challenges, this paper proposes a method to improve the segmentation performance of semantics using thermal images. By using temperature as a characteristic to distinguish different materials, the proposed method improves the accuracy of the segmentation model and has a high potential for automated construction and visual monitoring.
AB - Semantic segmentation is receiving increasing attention from researchers. Many emerging applications require accurate and efficient segmentation mechanisms, and this need coincides with the rise of almost all fields related to computer vision, especially in the construction industry. Building a semantic segmentation model for accurate and rich semantic information to identify, classify and segment building/structural components in construction site scenarios can greatly improve the productivity and informatization of the construction industry and has great potential for automated construction and visual monitoring. However, the accuracy of traditional image recognition technology is low, and it is difficult to adapt to the complex environment, and the illumination and Angle factors will affect the reliability of the final model. To address these challenges, this paper proposes a method to improve the segmentation performance of semantics using thermal images. By using temperature as a characteristic to distinguish different materials, the proposed method improves the accuracy of the segmentation model and has a high potential for automated construction and visual monitoring.
KW - Automated construction
KW - Semantic segmentation
KW - Thermal images
UR - http://www.scopus.com/inward/record.url?scp=85189556988&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7965-3_43
DO - 10.1007/978-981-99-7965-3_43
M3 - Conference Proceeding
AN - SCOPUS:85189556988
SN - 9789819979646
T3 - Lecture Notes in Civil Engineering
SP - 499
EP - 509
BT - Towards a Carbon Neutral Future - The Proceedings of The 3rd International Conference on Sustainable Buildings and Structures
A2 - Papadikis, Konstantinos
A2 - Zhang, Cheng
A2 - Tang, Shu
A2 - Liu, Engui
A2 - Di Sarno, Luigi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Sustainable Buildings and Structures, ICSBS 2023
Y2 - 17 August 2023 through 20 August 2023
ER -