Segmentation of Surface Cracks Based on a Fully Convolutional Neural Network and Gated Scale Pooling

König, Jacob; Jenkins, M.; Barrie, Peter; Mannion, Mike; Morison, Gordon

doi:10.23919/eusipco.2019.8902341

Cited by 7 publications

(6 citation statements)

References 22 publications

(35 reference statements)

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“…The crack-to-noncrack pixel ratio for this data set is 1:312. 51 We again used a 70:15:15 split for training, validation, and testing.…”

Section: Datasetsmentioning

confidence: 99%

Crack segmentation in the wild using convolutional neural networks and bootstrapping

Ahmad,

Gharehbaghi,

et al. 2023

Earthquake Engineering and Resilience

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Computer vision and deep learning methods have found numerous practical applications over the last decade. The field of structural health monitoring has greatly benefited from such advancements. Accurate crack detection and segmentation are a critical part of structural health monitoring and assessment. In the past decade, researchers have developed different computer vision and deep learning methods to address this challenging task. In this article, we propose to use convolutional neural networks and bootstrapping to improve crack segmentation in the wild. Specifically, we investigate fully convolutional network (FCN) and DeepLabV3 with different ResNet architectures as the back bone and assess their performance. A unique feature of this work is the use of bootstrapping with a segmentation network and image augmentation. Bootstrapping is an important component of our proposed methodology for better extracting features from the datasets. We also incorporate superpixel pooling for FCN which improves performance. We assess the performance of our method using five publicly available datasets, which include a wide variety of crack images such as thin pavement cracks, tree shadows on cracks, cracks in asphalt roads, concrete wall cracks, wide cracks on concrete surfaces, and so on. A comparative study is also performed with several established methods in the literature. Results indicate that overall, our proposed method outperforms other state‐of‐the‐art methods for crack segmentation.

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“…The crack-to-noncrack pixel ratio for this data set is 1:312. 51 We again used a 70:15:15 split for training, validation, and testing.…”

Section: Datasetsmentioning

confidence: 99%

Crack segmentation in the wild using convolutional neural networks and bootstrapping

Ahmad,

Gharehbaghi,

et al. 2023

Earthquake Engineering and Resilience

View full text Add to dashboard Cite

show abstract

“…In Kobayashi (2018), the author extended U-Net and applied an optimization method based on the F 1 -score. König et al used Gated Scale Pooling as a pooling operation in König et al (2019b) and in König et al (2019a) they applied residual connection and attention-based gating mechanism to pass through important activations. Chen et al applied U-Net with global context blocks (Chen et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…used Gated Scale Pooling as a pooling operation in König et al. (2019b) and in König et al. (2019a) they applied residual connection and attention‐based gating mechanism to pass through important activations.…”

Section: Related Workmentioning

confidence: 99%

A sigmoid‐optimized encoder–decoder network for crack segmentation with copy‐edit‐paste transfer learning

Çelik

König

2022

Computer aided Civil Eng

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The automatic recognition of cracks is an essential requirement for the cost‐efficient maintenance of concrete structures, such as bridges, buildings, and roads. It should allow the localization and the determination of the crack type and the evaluation of the crack severity by providing information on the shape, orientation, and crack area and width. The first step in this direction is the automatized segmentation of cracks. This paper provides a concrete crack data set (370 images) and proposes two solutions that achieve the best results on two different crack data sets. Our first solution concerns the segmentation architecture. We provide an encoder–decoder‐based network with a particular interconnection of layers between the encoder and decoder parts that outperforms several other methods. In addition, this network is enhanced by squeeze‐and‐excitation blocks equipped with a modified sigmoid activation function. We introduce a stretch coefficient into the sigmoid function and declare it a trainable parameter, allowing more differentiated calibration of the feature map during network training. Our second solution concerns kernel initialization by transfer learning (TL). We propose the Copy‐Edit‐Paste Transfer Learning (CEP TL). By copying, geometric editing, and pasting crack masks onto new concrete background images, we generate thousands of semisynthetic images used to pretrain the network. This CEP TL method increases model performance with significant differences. For data set A (ours), we achieve F1‐scores 76.06 ± 0.06% without CEP TL and 92.32 ± 0.82% with CEP TL. For data set B (DeepCrack data set), we achieve F1‐scores 88.56 ± 0.01% without CEP TL and 90.59 ± 0.80% with CEP TL.

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“…Sun et al [ 56 ] defined an encoder–decoder architecture with skip connections to combine multi-scale features at various levels for crack segmentation. Konig et al [ 57 ] used a fully convolutional, U-Net based [ 36 ] neural network with a pooling function, called the gated scale pooling operation, to merge multi-scale features from different layers of the model. These studies aimed to exploit multi-scale features through skip connections in a CNN model.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Eslami

Yun

2021

Sensors

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Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

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Segmentation of Surface Cracks Based on a Fully Convolutional Neural Network and Gated Scale Pooling

Cited by 7 publications

References 22 publications

Crack segmentation in the wild using convolutional neural networks and bootstrapping

Crack segmentation in the wild using convolutional neural networks and bootstrapping

A sigmoid‐optimized encoder–decoder network for crack segmentation with copy‐edit‐paste transfer learning

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

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