Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images

• URL: http://arxiv.org/abs/2310.08538v1
• Date: Thu, 12 Oct 2023 17:28:06 GMT
• Title: Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images
• Authors: Neema Jakisa Owor, Hang Du, Abdulateef Daud, Armstrong Aboah, Yaw Adu-Gyamfi
• Abstract summary: This study develops a unified multi-tasking model that predicts the Pavement Condition Index directly from a top-down pavement image. By multitasking, we are able to extract features from the detection and segmentation heads for automatically estimating the PCI directly from the images. The model performs very well on our benchmarked and open pavement distress dataset.
• Score: 8.64316207086894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Pavement Condition Index (PCI) is a widely used metric for evaluating pavement performance based on the type, extent and severity of distresses detected on a pavement surface. In recent times, significant progress has been made in utilizing deep-learning approaches to automate PCI estimation process. However, the current approaches rely on at least two separate models to estimate PCI values -- one model dedicated to determining the type and extent and another for estimating their severity. This approach presents several challenges, including complexities, high computational resource demands, and maintenance burdens that necessitate careful consideration and resolution. To overcome these challenges, the current study develops a unified multi-tasking model that predicts the PCI directly from a top-down pavement image. The proposed architecture is a multi-task model composed of one encoder for feature extraction and four decoders to handle specific tasks: two detection heads, one segmentation head and one PCI estimation head. By multitasking, we are able to extract features from the detection and segmentation heads for automatically estimating the PCI directly from the images. The model performs very well on our benchmarked and open pavement distress dataset that is annotated for multitask learning (the first of its kind). To our best knowledge, this is the first work that can estimate PCI directly from an image at real time speeds while maintaining excellent accuracy on all related tasks for crack detection and segmentation.

Related papers

• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• A Multi-task Framework for Infrared Small Target Detection and Segmentation [9.033048310220346]
  We propose a novel end-to-end framework for infrared small target detection and segmentation. We use UNet as the backbone to maintain resolution and semantic information. We develop a multi-task framework for infrared small target detection and segmentation.
  arXiv  Detail & Related papers  (2022-06-14T15:43:34Z)
• Sample and Computation Redistribution for Efficient Face Detection [137.19388513633484]
  Training data sampling and computation distribution strategies are the keys to efficient and accurate face detection. scrfdf34 outperforms the best competitor, TinaFace, by $3.86%$ (AP at hard set) while being more than emph3$times$ faster on GPUs with VGA-resolution images.
  arXiv  Detail & Related papers  (2021-05-10T23:51:14Z)
• Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection [5.735035463793008]
  Drone imagery is increasingly used in automated inspection for infrastructure surface defects. This paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements.
  arXiv  Detail & Related papers  (2021-04-21T13:07:58Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

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.