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A platform for research: civil engineering, architecture and urbanism
Semantic Segmentation-based Visual Detection of Construction Objects on Oversized Excavation Sites
https://orcid.org/http://orcid.org/0000-0002-5507-1645
The increasing exploitation of underground space has resulted in numerous oversized excavation projects which entail widely distributed risk factors and therefore demand a real-time risk management without blind spots. To facilitate vision-based risk identification and management, this study develops a comprehensive image dataset labeled in semantic mask level and an enhanced semantic segmentation algorithm to enable visual detection of the precise boundaries of construction objects on oversized excavation sites. Taking an oversized deep excavation project in Ningbo, Zhejiang Province, China as the case study, we first created an image dataset comprising real images, synthetic images generated from BIM, and web images crawled from the Internet. Ten classes of objects in construction activities of excavation sites, including worker, machine, and structure were selected as the target objects and finely annotated. A DeepLabv3+ algorithm modified with a lightweight MobileNetV2 backbone network and sub-pixel convolution (MobileNetV2-s) was employed on the developed dataset. Moreover, a compound loss function and transfer learning technique were leveraged for better algorithm training. Results demonstrate that MobileNetV2 achieves a mIoU of 69.67% and a mPA of 83.81% with an inference speed of 37.17 frames per second at a resolution of 1280 × 720 pixels, which strikes an optimal balance between performance and efficiency. The present study offers a promising solution for more efficient and reliable vision-based risk identification and management in oversized excavation projects.
Semantic Segmentation-based Visual Detection of Construction Objects on Oversized Excavation Sites
https://orcid.org/http://orcid.org/0000-0002-5507-1645
The increasing exploitation of underground space has resulted in numerous oversized excavation projects which entail widely distributed risk factors and therefore demand a real-time risk management without blind spots. To facilitate vision-based risk identification and management, this study develops a comprehensive image dataset labeled in semantic mask level and an enhanced semantic segmentation algorithm to enable visual detection of the precise boundaries of construction objects on oversized excavation sites. Taking an oversized deep excavation project in Ningbo, Zhejiang Province, China as the case study, we first created an image dataset comprising real images, synthetic images generated from BIM, and web images crawled from the Internet. Ten classes of objects in construction activities of excavation sites, including worker, machine, and structure were selected as the target objects and finely annotated. A DeepLabv3+ algorithm modified with a lightweight MobileNetV2 backbone network and sub-pixel convolution (MobileNetV2-s) was employed on the developed dataset. Moreover, a compound loss function and transfer learning technique were leveraged for better algorithm training. Results demonstrate that MobileNetV2 achieves a mIoU of 69.67% and a mPA of 83.81% with an inference speed of 37.17 frames per second at a resolution of 1280 × 720 pixels, which strikes an optimal balance between performance and efficiency. The present study offers a promising solution for more efficient and reliable vision-based risk identification and management in oversized excavation projects.
Semantic Segmentation-based Visual Detection of Construction Objects on Oversized Excavation Sites
https://orcid.org/http://orcid.org/0000-0002-5507-1645
The increasing exploitation of underground space has resulted in numerous oversized excavation projects which entail widely distributed risk factors and therefore demand a real-time risk management without blind spots. To facilitate vision-based risk identification and management, this study develops a comprehensive image dataset labeled in semantic mask level and an enhanced semantic segmentation algorithm to enable visual detection of the precise boundaries of construction objects on oversized excavation sites. Taking an oversized deep excavation project in Ningbo, Zhejiang Province, China as the case study, we first created an image dataset comprising real images, synthetic images generated from BIM, and web images crawled from the Internet. Ten classes of objects in construction activities of excavation sites, including worker, machine, and structure were selected as the target objects and finely annotated. A DeepLabv3+ algorithm modified with a lightweight MobileNetV2 backbone network and sub-pixel convolution (MobileNetV2-s) was employed on the developed dataset. Moreover, a compound loss function and transfer learning technique were leveraged for better algorithm training. Results demonstrate that MobileNetV2 achieves a mIoU of 69.67% and a mPA of 83.81% with an inference speed of 37.17 frames per second at a resolution of 1280 × 720 pixels, which strikes an optimal balance between performance and efficiency. The present study offers a promising solution for more efficient and reliable vision-based risk identification and management in oversized excavation projects.
Semantic Segmentation-based Visual Detection of Construction Objects on Oversized Excavation Sites
Lecture Notes in Civil Engineering
Wu, Wei (editor) / Leung, Chun Fai (editor) / Zhou, Yingxin (editor) / Li, Xiaozhao (editor) / Yang, Yi-Feng (author) / Liao, Shao-Ming (author) / Wang, Wei (author)
Conference of the Associated research Centers for the Urban Underground Space ; 2023 ; Boulevard, Singapore
2024-07-10
7 pages
Article/Chapter (Book)
Electronic Resource
English
Layered and segmented earth excavation method for oversized foundation pit
| European Patent Office | 2023