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A platform for research: civil engineering, architecture and urbanism
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 (author) / Han, Wei (author) / Mo, Sicheng (author) / Cai, Ting (author) / Gong, Yijing (author) / Li, Yin (author) / Zhu, Zhenhua (author)
2023-06-10
Article (Journal)
Electronic Resource
English
A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture
| DOAJ | 2022