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Floor plan is an important communication tool between architects, construction engineers, and clients for a building project. Estimation of building features from a floor plan image is often a time-consuming task. Automatic analysis of floor plan images can significantly improve work efficiency and accuracy. A few research works have been reported in the literature on automated floor image analysis. However, the scope and performance of the existing techniques are limited. In this paper, a CNN-based technique, referred to as FloorNet, is proposed for the multiclass semantic segmentation of a floor plan. The proposed FloorNet has five modules: Encoder, Room type decoder, Room boundary decoder, Multiscale room boundary attention model and Floor classification. The proposed technique is evaluated using simple brochure type and complex architectural type floor plan images. Experimental results show that the proposed technique provides an improvement of 5–11% mIoU for semantic segmentation (for 9–11 classes) compared to the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Offshore drilling platforms (ODP) require effective deformation monitoring and damage detection in the wellhead space for operational maintenance. LiDAR technology, replacing traditional methods, plays a key role in damage detection through 3D reconstruction, and the accurate semantic segmentation of point clouds is fundamental to this process. Following advancements in deep learning, neural network models have emerged as effective tools for the semantic segmentation of point clouds in a range of settings. ODP point cloud data, characterized by large volumes, high density, class imbalance, and complex structures, present unique challenges for feature extraction. To address these challenges, this paper proposed a multi-scale bounding box (MSB-Box) feature extraction strategy based on spatial relation dynamic encoding (SRD-encoding) and a multi-scale point cloud down-sampling (MPDS) strategy to enhance computational efficiency. Utilizing these proposed strategies, we developed an ODP-Net, a deep learning neural network based on the attention mechanism. The ODP-Net's semantic segmentation performance was evaluated using a self-constructed ODP scene point cloud dataset and the S3DIS dataset. The results demonstrate that ODP-Net, based on MPDS and MSB-Box, outperforms other networks in diverse scenes, confirming its high accuracy and wide applicability in the semantic segmentation of point cloud data in ODP scenes. [Display omitted] • A deep learning network for semantic segmentation called ODP-Net based on the attention mechanisms is proposed. • MSB-Box feature extraction strategy and MPDS strategy are proposed to enhance ODP-Net's semantic segmentation accuracy. • A point cloud dataset of ODP scenes for deep learning semantic segmentation is constructed. [ABSTRACT FROM AUTHOR]

Semantic segmentation plays a crucial role in the fields of computer vision and computer graphics, with extensive applications in various practical scenarios. Significant progress has been made in semantic segmentation tasks using deep learning-based methods. However, most existing semantic segmentation algorithms focus on good weather conditions, and they face challenges in terms of accuracy and robustness when applied to rainy scenes due to factors such as raindrops, haze, and lighting variations. To address this issue, this paper proposes a rainy-day semantic segmentation method based on a two-stage progressive network. The proposed method consists of two modules: a rain removal module responsible for eliminating raindrops and haze from the input rainy images and restoring the basic structural information of the images, and a segmentation module that performs pixel-level semantic prediction on the rain-removed images. Specifically, the rain removal module introduces two progressive units with shared weights to gradually achieve rain removal. The segmentation module adopts an encoder–decoder architecture, utilizing down-sampling and deep asynchronous bottleneck units for encoding. It also introduces a dual attention-guided fusion module to aggregate channel attention information and spatial attention information, guiding the multiscale feature fusion process in the decoder. Experimental results demonstrate that this method effectively mitigates the influence of rain streaks on semantic segmentation, thereby improving segmentation performance and achieving more accurate and robust semantic segmentation results in rainy conditions. We will provide the code and datasets on https://github.com/zhang152267/TSPN. [ABSTRACT FROM AUTHOR]