scholarly journals Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lin Wang ◽  
Xingfu Wang ◽  
Ammar Hawbani ◽  
Yan Xiong ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Processing Efficiency ◽  
Semantic Image Segmentation ◽  
Average Improvement ◽  
Convolutional Encoders

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

Study on semantic image segmentation based on convolutional neural network

2017 ◽  
Vol 33 (6) ◽  
pp. 3397-3404 ◽  
Author(s):  
Lin-Hui Li ◽  
Bo Qian ◽  
Jing Lian ◽  
Wei-Na Zheng ◽  
Ya-Fu Zhou
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Semantic Image Segmentation

scholarly journals A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation

2019 ◽  
Vol 8 (12) ◽  
pp. 582 ◽  
Author(s):  
Gang Zhang ◽  
Tao Lei ◽  
Yi Cui ◽  
Ping Jiang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Feature Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Aerial Image ◽  
Sensing Applications ◽  
Edge Path

Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines.

scholarly journals Image Segmentation Using Encoder-Decoder with Deformable Convolutions

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1570
Author(s):  
Andreea Gurita ◽  
Irina Georgiana Mocanu
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Real Life ◽  
Semantic Segmentation ◽  
Essential Step ◽  
Augmentation Techniques ◽  
Made In

Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures that can suffer from noise or object obstruction. This paper proposes an architecture for semantic segmentation using a convolutional neural network based on the Xception model, which was previously used for classification. Different experiments were made in order to find the best performances of the model (eg. different resolution and depth of the network and data augmentation techniques were applied). Additionally, the network was improved by adding a deformable convolution module. The proposed architecture obtained a 76.8 mean IoU on the Pascal VOC 2012 dataset and 58.1 on the Cityscapes dataset. It outperforms SegNet and U-Net networks, both networks having considerably more parameters and also a higher inference time.

Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network

2021 ◽  
pp. 1-16
Author(s):  
Sumit Tripathi ◽  
Neeraj Sharma
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Discriminative Learning ◽  
Mr Images ◽  
Experienced Radiologist ◽  
Average Improvement ◽  
Medical Image Diagnosis

BACKGROUND: The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE: In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS: The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS: Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05. CONCLUSION: The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images.

scholarly journals R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Zuo ◽  
Songyu Chen ◽  
Zhifang Wang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Science ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Public Dataset

In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.

scholarly journals Deep Convolutional Neural Network untuk Mendeteksi Retak pada Permukaan Beton yang Memiliki Void

2021 ◽  
Vol 1 (1) ◽  
pp. 45-55
Author(s):  
Patrick Nicholas Hadinata ◽  
Djoni Simanta ◽  
Liyanto Eddy
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Semantic Image Segmentation

Convolutional neural network berbasis encoder-decoder telah dirancang dan dilatih menggunakan dataset eksternal untuk mendeteksi retak pada permukaan beton yang relatif sederhana. Namun, pada kenyataannya permukaan beton memiliki banyak fitur seperti void pada permukaan yang disebabkan oleh udara yang terperangkap saat proses pencampuran beton. Oleh karena itu, pada penelitian ini kemampuan convolutional neural network akan diteliti lebih lanjut untuk mendeteksi retak pada permukaan beton yang memiliki void. Tujuan pertama penelitian ini adalah menguji model yang dilatih dengan dataset eksternal pada permukaan beton ber-void. Jika model tidak berhasil membedakan void dengan retak, maka tujuan kedua penelitian ini adalah menyusun dataset pelatihan internal baru yang secara khusus membedakan void dengan retak, yang kemudian akan ditambahkan pada dataset eksternal untuk diinvestigasi performanya. Penelitian ini menggunakan arsitektur U-Net dan arsitektur DeepLabV3+ sebagai encoder-decoder untuk mengoperasikan semantic image segmentation. Model encoder-decoder yang dilatih dengan dataset eksternal tidak berhasil membedakan void dengan retak saat pengujian. Maka, dataset internal yang terdiri dari gambar beton ber-void dibentuk dan digabungkan dengan dataset eksternal. Dengan penambahan dataset internal yang baru, hasil pengujian menunjukkan bahwa model berhasil membedakan void dengan retak pada permukaan beton. U-Net mencapai nilai F1 sebesar 85,92%, sedangkan DeepLabV3+ mencapai nilai F1 sebesar 84,09%.

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