Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net

Yang, Xiaofei; Li, Xutao; Ye, Yunming; Lau, Raymond Y. K.; Zhang, Xiaofeng; Huang, Xiaohui

doi:10.1109/tgrs.2019.2912301

Cited by 182 publications

(101 citation statements)

References 36 publications

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“…By refining the CNN architecture, Gao et al [35] proposed the refined deep residual convolutional neural network (RDRCNN) to enable it to detect the road area more accurately. To solve the problems of noise, occlusion, and complex background, Yang et al [36] successfully designed an RCNN unit and integrated it into the U-Net architecture. The significant advantage of this unit is that it retains detailed low-level spatial characteristics.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

et al. 2019

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Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

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Section: Introductionmentioning

confidence: 99%

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

et al. 2019

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“…-RCNNUNet. Recursive image segmentation with post-processing for graph extraction [33]. -MultiBranch.…”

Section: Real Datamentioning

confidence: 99%

“…It has been tackled almost since the inception of the field in the 1970's [4,28,26,13]. Yet, it is still open and is addressed by many recent papers [23,22,9,21,19,5,25,10,6,24,33]. One pitfall however, is that the metrics used to gauge performance often prove to be inconsistent.…”

Section: Introductionmentioning

confidence: 99%

Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images

Citraro

Koziński

Fua

2020

Lecture Notes in Computer Science

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Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application. We show that these inconsistencies stem from design flaws that make the metrics insensitive to whole classes of errors. This insensitivity is undesirable in metrics intended for capturing overall general quality of road reconstructions. In particular, the scores do not reflect the time needed for a human to fix the errors, because each one has to be fixed individually. To provide more reliable evaluation, we design three new metrics that are sensitive to all classes of errors. This sensitivity makes them more consistent even though they use very different approaches to comparing ground-truth and reconstructed road networks. We use both synthetic and real data to demonstrate this and advocate the use of these corrected metrics as a tool to gauge future progress.

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“…At present, Unet is widely used in medical image segmentation . Besides, it still has some applications in other research fields . SegNet also has many applications in the field of medical image segmentation .…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25][26] Besides, it still has some applications in other research fields. 27 SegNet also has many applications in the field of medical image segmentation. [28][29][30][31] Moreover, its application in semantic segmentation is relatively mature.…”

Section: Introductionmentioning

confidence: 99%

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

et al. 2019

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Purpose To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented. Methods An ensemble model of pretrained convolutional neural networks was proposed, using Unet‐VGG, SegNet‐VGG, and PSPNet. Firstly, the Unet‐VGG is obtained by the first 10 layers of VGG16 as the contraction path of the left half of the Unet. Then, the SegNet‐VGG is acquired by fine‐tuned transfer learning VGG16, using the first 13 layers of VGG16 as the encoder of the SegNet and combined the original decoder of the SegNet. By adjusting the input size of the Unet‐VGG, SegNet‐VGG, and PSPNet, the preprocessed data can be correctly fed to the three network models. The three models are used as the basic trainer to train and segment the datasets. Based on the ensemble learning algorithm, the weight voting method is used to ensemble the segmentation results corresponding to single basic trainer. Results Both IoU and DICE similarity score were used to evaluate the segmentation quality for cvc300 with 300 images, CVC‐ClinicDB with 612 images, and ETIS‐LaribPolypDB with 196 images. From the experimental results, the IoU and DICE obtained by the proposed method for the cvc300 datasets can reach up to 96.16% and 98.04%, respectively, the IoU and DICE for the CVC‐ClinicDB datasets can reach up to 96.66% and 98.30%, respectively, whereas the IoU and DICE for the ETIS‐LaribPolypDB datasets can reach up to 96.95% and 98.45%, respectively. Evaluation of the IoU and DICE in our methods shows higher accuracy than previous methods. Conclusions The experimental results show that the proposed method improved correspondingly in IoU and DICE compared to a single basic trainer. The range of improvement is 1.98%–6.38%. The proposed ensemble learning succeeds in automatic polyp segmentation, which potentially helps to establish more polyp datasets.

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Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net

Cited by 182 publications

References 36 publications

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

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