Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Transformer-based automated segmentation of recycling materials for semantic understanding in construction
Abstract Construction sites are incorporating cameras to gather imagery data for project management. While transformer-based deep models show promise in recognizing construction objects and understanding the environment, their use in construction images is largely unexplored. This paper presents a systematic evaluation of three state-of-the-art transformer-based models for automatic segmentation and recognition of construction images. Further, a two-stage model ensembling strategy based on model averaging and probability weighting is introduced and implemented for performance improvement. A dataset containing five classes of recycling materials on construction sites is created as a benchmark to compare their performance. The comparison results indicate the ensemble model could achieve encouraging results with a mIoU of 82.36% and mPA of 90.30%, which demonstrate superior segmentation performance on construction images.
Highlights Present a systematic evaluation of state-of-the-art transformer-based segmentation models on construction images. Propose a two-stage model ensembling strategy for performance improvement. Create a dataset containing 5 classes of recycling materials as a benchmark. Achieve segmentation results with a mIoU of 82.36% and mPA of 90.30% by the proposed ensemble model.
Transformer-based automated segmentation of recycling materials for semantic understanding in construction
Abstract Construction sites are incorporating cameras to gather imagery data for project management. While transformer-based deep models show promise in recognizing construction objects and understanding the environment, their use in construction images is largely unexplored. This paper presents a systematic evaluation of three state-of-the-art transformer-based models for automatic segmentation and recognition of construction images. Further, a two-stage model ensembling strategy based on model averaging and probability weighting is introduced and implemented for performance improvement. A dataset containing five classes of recycling materials on construction sites is created as a benchmark to compare their performance. The comparison results indicate the ensemble model could achieve encouraging results with a mIoU of 82.36% and mPA of 90.30%, which demonstrate superior segmentation performance on construction images.
Highlights Present a systematic evaluation of state-of-the-art transformer-based segmentation models on construction images. Propose a two-stage model ensembling strategy for performance improvement. Create a dataset containing 5 classes of recycling materials as a benchmark. Achieve segmentation results with a mIoU of 82.36% and mPA of 90.30% by the proposed ensemble model.
Transformer-based automated segmentation of recycling materials for semantic understanding in construction
Wang, Xin (Autor:in) / Han, Wei (Autor:in) / Mo, Sicheng (Autor:in) / Cai, Ting (Autor:in) / Gong, Yijing (Autor:in) / Li, Yin (Autor:in) / Zhu, Zhenhua (Autor:in)
10.06.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch