A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis

• URL: http://arxiv.org/abs/2209.02141v1
• Date: Mon, 5 Sep 2022 21:02:08 GMT
• Title: A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
• Authors: Chenyu Zhang, Muhammad Monjurul Karim, Zhaozheng Yin, Ruwen Qin
• Abstract summary: This article aims to determine a suitable deep neural network (DNN) for parsing multiclass bridge elements in inspection images. With data augmentation and a training sample of 130 images, a pre-trained HRNet is efficiently transferred to the task of structural element parsing.
• Score: 9.635496805334899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aerial robots such as drones have been leveraged to perform bridge inspections. Inspection images with both recognizable structural elements and apparent surface defects can be collected by onboard cameras to provide valuable information for the condition assessment. This article aims to determine a suitable deep neural network (DNN) for parsing multiclass bridge elements in inspection images. An extensive set of quantitative evaluations along with qualitative examples show that High-Resolution Net (HRNet) possesses the desired ability. With data augmentation and a training sample of 130 images, a pre-trained HRNet is efficiently transferred to the task of structural element parsing and has achieved a 92.67% mean F1-score and 86.33% mean IoU.

Related papers

• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Heuristic Hyperparameter Choice for Image Anomaly Detection [0.3867363075280543]
  Anomaly detection in images is a fundamental computer vision problem by deep learning neural network. Models are usually pretrained on a large dataset for classification tasks such as ImageNet. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features.
  arXiv  Detail & Related papers  (2023-07-20T19:20:35Z)
• A Light-weight Deep Learning Model for Remote Sensing Image Classification [70.66164876551674]
  We present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC) By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher-student models outperforms the state-of-the-art systems.
  arXiv  Detail & Related papers  (2023-02-25T09:02:01Z)
• 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)
• Knowledge Distillation for Oriented Object Detection on Aerial Images [1.827510863075184]
  We present a model compression method for rotated object detection on aerial images by knowledge distillation, namely KD-RNet. The experimental result on a large-scale aerial object detection dataset (DOTA) demonstrates that the proposed KD-RNet model can achieve improved mean-average precision (mAP) with reduced number of parameters, at the same time, KD-RNet boost the performance on providing high quality detections with higher overlap with groundtruth annotations.
  arXiv  Detail & Related papers  (2022-06-20T14:24:16Z)
• 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)
• One-Shot Object Affordance Detection in the Wild [76.46484684007706]
  Affordance detection refers to identifying the potential action possibilities of objects in an image. We devise a One-Shot Affordance Detection Network (OSAD-Net) that estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods.
  arXiv  Detail & Related papers  (2021-08-08T14:53:10Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection [7.151552353494974]
  In this paper, attention-based top-down and bottom-up architecture is developed to accomplish multi-scale feature fusion. A novel object detector is proposed, called BAF-Detector, which embeds BAFPN into Region Proposal Network (RPN) in Faster RCNN+FPN. The experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method achieves 98.70% (F-measure), 88.07% (mAP), and 73.29% (IoU) in terms of multi-scale defects classification and
  arXiv  Detail & Related papers  (2020-12-19T08:48:14Z)
• Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration [65.11022516031463]
  We present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. In this paper, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Our presented DIN can be trained end-to-end and applied to various image restoration tasks.
  arXiv  Detail & Related papers  (2020-10-29T15:32:00Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

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.