Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Clouds

• URL: http://arxiv.org/abs/2409.16381v1
• Date: Tue, 24 Sep 2024 18:28:41 GMT
• Title: Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Clouds
• Authors: Asad Ur Rahman, Vedhus Hoskere,
• Abstract summary: National Bridge Inspection Standards require detailed element-level bridge inspections. Traditionally, inspectors manually assign ratings by rating structural components based on damage. We propose a novel approach for generating synthetic data using three distinct methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The National Bridge Inspection Standards require detailed element-level bridge inspections. Traditionally, inspectors manually assign condition ratings by rating structural components based on damage, but this process is labor-intensive and time-consuming. Automating the element-level bridge inspection process can facilitate more comprehensive condition documentation to improve overall bridge management. While semantic segmentation of bridge point clouds has been studied, research on instance segmentation of bridge elements is limited, partly due to the lack of annotated datasets, and the difficulty in generalizing trained models. To address this, we propose a novel approach for generating synthetic data using three distinct methods. Our framework leverages the Mask3D transformer model, optimized with hyperparameter tuning and a novel occlusion technique. The model achieves state-of-the-art performance on real LiDAR and photogrammetry bridge point clouds, respectively, demonstrating the potential of the framework for automating element-level bridge inspections.

Related papers

• An attempt to generate new bridge types from latent space of variational autoencoder [2.05750372679553]
  Variational autoencoder can combine two bridge types on the basis of the original of human into one that is a new bridge type. Generative artificial intelligence technology can assist bridge designers in bridge-type innovation, and can be used as copilot.
  arXiv  Detail & Related papers  (2023-11-02T08:18:37Z)
• AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers [94.11915008006483]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion. Our method attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI.
  arXiv  Detail & Related papers  (2023-01-11T16:14:12Z)
• A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images [1.476043573732074]
  The vast network of bridges in the United States raises a high requirement for its maintenance and rehabilitation. The massive cost of visual inspection to assess the conditions of the bridges turns out to be a burden to some extent. This paper develops a multitask deep neural network that fully utilizes such interdependence between bridge elements and defects.
  arXiv  Detail & Related papers  (2022-09-06T02:48:15Z)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)
• Learning Local Displacements for Point Cloud Completion [93.54286830844134]
  We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure. We evaluate both architectures on object and indoor scene completion tasks, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T18:31:37Z)
• A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos [6.75013674088437]
  This paper develops an assistive intelligence model for segmenting multiclass bridge elements from inspection videos. A Mask Region-based Convolutional Neural Network (Mask R-CNN) pre-trained on a large public dataset was transferred to the new task. A semi-supervised self-training (S$3$T) method was developed to engage experienced inspectors in refining the network.
  arXiv  Detail & Related papers  (2021-09-10T19:22:45Z)
• HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis [9.261126434781744]
  We introduce a new framework that transfers the model learned from one bridge to diagnose damage in another bridge without any labels from the target bridge. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features. We evaluate our framework on experimental data collected from 2 bridges and 3 vehicles.
  arXiv  Detail & Related papers  (2021-07-23T19:39:32Z)
• When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model [87.25037167380522]
  We propose a model that is accurate, robust, efficient, generalizable, and end-to-end trainable. In order to achieve a better accuracy, we propose two lightweight modules. DQInit dynamically initializes the queries of decoder from the inputs, enabling the model to achieve as good accuracy as the ones with multiple decoder layers. QAMem is designed to enhance the discriminative ability of queries on low-resolution feature maps by assigning separate memory values to each query rather than a shared one.
  arXiv  Detail & Related papers  (2021-05-27T13:51:42Z)
• UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [52.487469544343305]
  Methods for object detection and segmentation rely on large scale instance-level annotations for training. We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
  arXiv  Detail & Related papers  (2020-06-12T22:45:47Z)
• End-to-End Object Detection with Transformers [88.06357745922716]
  We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss.
  arXiv  Detail & Related papers  (2020-05-26T17:06:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.