Deep Metric Learning-Based for Multi-Target Few-Shot Pavement Distress Classification

Dong, Hongwen; Song, Kechen; Wang, Qi; Yan, Yunhui; Jiang, Peng

doi:10.1109/tii.2021.3090036

Cited by 78 publications

(14 citation statements)

References 37 publications

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“…This new architecture results in a higher ability to train deeper networks in comparison to networks without the skip connection. ResNet shows high potential in image recognition tasks [12], [13]. Several architectures of ResNet are available with diverse depths, such as ResNet-18, ResNet-34, etc.…”

Section: A Classifiermentioning

confidence: 99%

Recognition of Defective Mineral Wool Using Pruned ResNet Models

Rafiei¹,

Tran²,

Iosifidis³

2022

Preprint

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Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recognize defective products is critical. For this purpose, we developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and nondefective samples. Afterward, we developed several recognition models based on the ResNet architecture to find the most efficient model. In order to have a light-weight and fast inference model for real-life applicability, two structural pruning methods are applied to the classifiers. Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training. As a result, we obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.

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Section: A Classifiermentioning

confidence: 99%

Recognition of Defective Mineral Wool Using Pruned ResNet Models

Rafiei¹,

Tran²,

Iosifidis³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Inspired by the recent remarkable successes of deep learning in extensive applications, simple and efficient convolutional neural networks (CNN) based pavement distress analysis methods have gradually become the mainstream in recent years. In general, these methods can be divided into three parts according to the task objective: pavement distress segmentation [12,22,23,24], pavement distress location [25,26], and pavement distress classification [13,27,28]. Among them, pixel-based pavement distress segmentation is a hot research field.…”

Section: A Image-based Pavement Distress Analysismentioning

confidence: 99%

“…For pavement distress classification, Dong et al [27] propose a metric-learning based method for multi-target few-show pavement distress classification on the dataset which includes 10 different kinds of distress. In [28], discriminative superfeatures constructed by the multi-level context information from the CNN is used to determine whether there is distress in the pavement image and recognize the type of the distress.…”

Section: A Image-based Pavement Distress Analysismentioning

confidence: 99%

Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild

Hu¹,

Tang²,

Huang³

et al. 2022

Preprint

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Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. To fully exploit the resolution and scale information, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution, and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency.

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“…As for metric-learning-based few-shot learning models, the major idea is to learn a unified metric or matching function; e.g., a relation network [32] is proposed to measure features by neural networks. Dong et al [33] developed a new metric loss function for few-shot learning, which can enlarge the distance between different categories and reduce the distance of the same categories. Few-shot learning has been developed for many computer vision tasks, including image classification [34,35], object detection, segmentation [36], and so on.…”

Section: Few-shot Learningmentioning

confidence: 99%

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

et al. 2021

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Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.

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Deep Metric Learning-Based for Multi-Target Few-Shot Pavement Distress Classification

Cited by 78 publications

References 37 publications

Recognition of Defective Mineral Wool Using Pruned ResNet Models

Recognition of Defective Mineral Wool Using Pruned ResNet Models

Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

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