SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

Badrinarayanan, Vijay; Handa, Ankur; Cipolla, Roberto

doi:10.48550/arxiv.1505.07293

Cited by 121 publications

(232 citation statements)

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“…Moreover, in the crack patch extraction step, to further reduce overall processing time and suppress the identified crack patches but keep the patches with significant local edge texture, a non-maximum image patch suppression strategy is proposed. Once the crack patches are detected, SegNet [87] architecture is employed to output the crack mask. In [88], firstly, the pre-trained AlexNet on ImageNet data set is used to classify the image patches into the crack, sealed crack, and background classes.…”

Section: Hybrid Semantic Segmentationmentioning

confidence: 99%

“…The encoder-decoder structure has been abundantly applied to perform crack segmentation. Among various well-known architectures that have been proposed for performing SS in the computer vision area, U-Net [99], SegNet [87], and FC-DenseNet [100] have been the most considered in the crack detection area. It must be added that to achieve higher accuracy in the SS setting, it is important to let the contextual information flow in the architecture [101].…”

Section: Pure Semantic Segmentationmentioning

confidence: 99%

“…SegNet is an encoder-decoder architecture where the encoder is the VGG-16 [38] architecture without the fully connected layers [87]. The main feature of SegNet is that during the decoding, the max-pooling indices at the corresponding encoder layer are recalled and used to up-sample in the decoder module.…”

Section: Pure Semantic Segmentationmentioning

confidence: 99%

“…Particularly, they fused the sparse features at smaller scales with continuous features at larger scales to have a better performance for the task of crack segmentation. It is stated in [112] that the proposed encoder-decoder structure in the study which is inspired by SegNet [87], FCN [89], and ZFNet [113] was the first application of deep learning to find cracks in black-box images. A pre-trained ResNet [33] on the ImageNet data set was employed as the encoder module.…”

Section: Pure Semantic Segmentationmentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

Hamishebahar

Guan

et al. 2022

Applied Sciences

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The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area.

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Section: Hybrid Semantic Segmentationmentioning

confidence: 99%

Section: Pure Semantic Segmentationmentioning

confidence: 99%

Section: Pure Semantic Segmentationmentioning

confidence: 99%

Section: Pure Semantic Segmentationmentioning

confidence: 99%

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A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

Hamishebahar

Guan

et al. 2022

Applied Sciences

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“…All of existing semantic segmentation approaches share the same goal to classify each pixel of a given image but differ in the network design, including low-resolution representations learning [28,6], high-resolution representations recovering [1,33,25], contextual aggregation schemes [47,50,48], feature fusion and refinement strategy [25,20,23,52,14]. Typically, method designs are dependent on their respective datasets and all the mentioned networks are developed by training on benchmark datasets such as Cityscapes [8], COCO [26] and VOC [13] where the inter-class boundary is clear even for the within-group categories (e.g., car and truck).…”

Section: Semantic Segmentation Networkmentioning

confidence: 99%

ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images

Erfani¹,

Wu²,

Guo³

et al. 2021

Preprint

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Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present AT-LANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS in detail and evaluate several state-of-the-art semantic segmentation networks on our benchmark. In addition, a novel deep neural network, AQUANet, is developed for waterbody semantic segmentation by processing the aquatic and non-aquatic regions in two different paths. AQUANet also incorporates low-level feature modulation and cross-path modulation for enhancing feature representation. Experimental results show that the proposed AQUANet outperforms other state-of-the-art semantic segmentation networks on ATLANTIS. We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering.

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A classification method based on encoder‐decoder structure with paper content

Yin

Ouyang

et al. 2020

Concurrency and Computation

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The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder-decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information.But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder-decoder structure is formed with labels and words. This improves the original paper classification method based on encoder-decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder-decoder structure.Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results.

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

Cited by 121 publications

References 0 publications

A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images

A classification method based on encoder‐decoder structure with paper content

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