Convolutional neural networks for crack detection on flexible road pavements

• URL: http://arxiv.org/abs/2304.02933v1
• Date: Thu, 6 Apr 2023 08:46:30 GMT
• Title: Convolutional neural networks for crack detection on flexible road pavements
• Authors: Hermann Tapamo, Anna Bosman, James Maina and Emile Horak
• Abstract summary: This study performs a comparison of six state-of-the-art convolutional neural network models for the purpose of crack detection. The highest recorded accuracy was 98%, achieved by the ResNet and VGG16 models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Flexible road pavements deteriorate primarily due to traffic and adverse environmental conditions. Cracking is the most common deterioration mechanism; the surveying thereof is typically conducted manually using internationally defined classification standards. In South Africa, the use of high-definition video images has been introduced, which allows for safer road surveying. However, surveying is still a tedious manual process. Automation of the detection of defects such as cracks would allow for faster analysis of road networks and potentially reduce human bias and error. This study performs a comparison of six state-of-the-art convolutional neural network models for the purpose of crack detection. The models are pretrained on the ImageNet dataset, and fine-tuned using a new real-world binary crack dataset consisting of 14000 samples. The effects of dataset augmentation are also investigated. Of the six models trained, five achieved accuracy above 97%. The highest recorded accuracy was 98%, achieved by the ResNet and VGG16 models. The dataset is available at the following URL: https://zenodo.org/record/7795975

Related papers

• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure [52.2025114590481]
  We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status.
  arXiv  Detail & Related papers  (2024-09-04T16:47:16Z)
• Multiple data sources and domain generalization learning method for road surface defect classification [2.9109581496560044]
  We propose a method for classifying road surface defects using camera images. We present a domain generalization training algorithm for developing a generalized model. The results show that our method can efficiently classify road surface defects on previously unseen data.
  arXiv  Detail & Related papers  (2024-07-14T13:37:47Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Detection of Pavement Cracks by Deep Learning Models of Transformer and UNet [9.483452333312373]
  In recent years, the emergence and development of deep learning techniques have shown great potential to facilitate surface crack detection. In this study, we investigated nine promising models to evaluate their performance in pavement surface crack detection by model accuracy, computational complexity, and model stability. We find that transformer-based models generally are easier to converge during the training process and have higher accuracy, but usually exhibit more memory consumption and low processing efficiency.
  arXiv  Detail & Related papers  (2023-04-25T06:07:49Z)
• Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network [0.0]
  This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. CrackPropNet learns to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. It can be applied to characterize the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.
  arXiv  Detail & Related papers  (2023-03-10T14:45:37Z)
• EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals [160.57870373052577]
  A lack of driver's vigilance is the main cause of most vehicle crashes. EEG has been reliable and efficient tool for drivers' drowsiness estimation.
  arXiv  Detail & Related papers  (2022-11-07T21:35:08Z)
• Road Rutting Detection using Deep Learning on Images [0.0]
  Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset.
  arXiv  Detail & Related papers  (2022-09-28T16:53:05Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Scalable Backdoor Detection in Neural Networks [61.39635364047679]
  Deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. We propose a novel trigger reverse-engineering based approach whose computational complexity does not scale with the number of labels, and is based on a measure that is both interpretable and universal across different network and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojaned models from pure models, which is an improvement over the current state-of-the art method.
  arXiv  Detail & Related papers  (2020-06-10T04:12:53Z)
• Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement [9.34360241512198]
  An ensemble of convolutional neural networks was employed to identify the structure of small cracks. For crack measurement, the crack length and width can be measure based on different crack types.
  arXiv  Detail & Related papers  (2020-02-08T22:15:11Z)
• Radioactive data: tracing through training [130.2266320167683]
  We propose a new technique, emphradioactive data, that makes imperceptible changes to this dataset such that any model trained on it will bear an identifiable mark. Given a trained model, our technique detects the use of radioactive data and provides a level of confidence (p-value) Our method is robust to data augmentation and backdoority of deep network optimization.
  arXiv  Detail & Related papers  (2020-02-03T18:41:08Z)

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.