# A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images

arXiv: https://arxiv.org/abs/2209.02190 · HTML: https://mlsanity.com/paper/2209.02190/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks](https://mlsanity.com/paper/1709.05932/) (2017)
- [Pedestrian Detection aided by Deep Learning Semantic Tasks](https://mlsanity.com/paper/1412.0069/) (2014)
- [A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos](https://mlsanity.com/paper/2109.05078/) (2021)
- [A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis](https://mlsanity.com/paper/2209.02141/) (2022)

## Built on by (most cited first, 2 of 2)

- [Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection](https://mlsanity.com/paper/2307.07643/) (2023)
- [KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning](https://mlsanity.com/paper/2508.08186/) (2025)

## Abstract (arXiv)

The vast network of bridges in the United States raises a high requirement for maintenance and rehabilitation. The massive cost of manual visual inspection to assess bridge conditions is a burden to some extent. Advanced robots have been leveraged to automate inspection data collection. Automating the segmentations of multiclass elements and surface defects on the elements in the large volume of inspection image data would facilitate an efficient and effective assessment of the bridge condition. Training separate single-task networks for element parsing (i.e., semantic segmentation of multiclass elements) and defect segmentation fails to incorporate the close connection between these two tasks. Both recognizable structural elements and apparent surface defects are present in the inspection images. This paper is motivated to develop a multitask deep learning model that fully utilizes such interdependence between bridge elements and defects to boost the model's task performance and generalization. Furthermore, the study investigated the effectiveness of the proposed model designs for improving task performance, including feature decomposition, cross-talk sharing, and multi-objective loss function. A dataset with pixel-level labels of bridge elements and corrosion was developed for model training and testing. Quantitative and qualitative results from evaluating the developed multitask deep model demonstrate its advantages over the single-task-based model not only in performance (2.59% higher mIoU on bridge parsing and 1.65% on corrosion segmentation) but also in computational time and implementation capability.