Optimizing FPGA-based Convolutional Encoder-Decoder Architecture for Semantic Segmentation

2019 ◽  
Author(s):  
Mengqi Yu ◽  
Hongzhi Huang ◽  
Hong Liu ◽  
Shuyi He ◽  
Fei Qiao ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Decoder Architecture ◽  
Convolutional Encoder

scholarly journals Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5630
Author(s):  
Chin Yii Eu ◽  
Tong Boon Tang ◽  
Cheng-Hung Lin ◽  
Lok Hua Lee ◽  
Cheng-Kai Lu
Keyword(s):  
Colorectal Cancer ◽  
Automatic Segmentation ◽  
Hospital Setting ◽  
Decoder Architecture ◽  
The Future ◽  
Sensitivity Specificity ◽  
Convolutional Encoder ◽  
Cancer Côlon ◽  
Aided Diagnosis ◽  
Better Than

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

scholarly journals DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

2021 ◽  
Vol 12 ◽  
Author(s):  
Talha Ilyas ◽  
Muhammad Umraiz ◽  
Abbas Khan ◽  
Hyongsuk Kim
Keyword(s):  
Feature Selection ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Dynamic Feature ◽  
Feature Maps ◽  
Adaptive Feature Selection ◽  
Convolutional Encoder ◽  
Time Segmentation ◽  
Flow Of Information ◽  
Segmentation Models

Autonomous harvesters can be used for the timely cultivation of high-value crops such as strawberries, where the robots have the capability to identify ripe and unripe crops. However, the real-time segmentation of strawberries in an unbridled farming environment is a challenging task due to fruit occlusion by multiple trusses, stems, and leaves. In this work, we propose a possible solution by constructing a dynamic feature selection mechanism for convolutional neural networks (CNN). The proposed building block namely a dense attention module (DAM) controls the flow of information between the convolutional encoder and decoder. DAM enables hierarchical adaptive feature fusion by exploiting both inter-channel and intra-channel relationships and can be easily integrated into any existing CNN to obtain category-specific feature maps. We validate our attention module through extensive ablation experiments. In addition, a dataset is collected from different strawberry farms and divided into four classes corresponding to different maturity levels of fruits and one is devoted to background. Quantitative analysis of the proposed method showed a 4.1% and 2.32% increase in mean intersection over union, over existing state-of-the-art semantic segmentation models and other attention modules respectively, while simultaneously retaining a processing speed of 53 frames per second.

scholarly journals Neural networks for topology optimization

2019 ◽  
Vol 34 (4) ◽  
pp. 215-223 ◽  
Author(s):  
Ivan Sosnovik ◽  
Ivan Oseledets
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
High Performance ◽  
Current Method ◽  
Optimization Methods ◽  
Proposed Model ◽  
Significant Acceleration ◽  
Decoder Architecture ◽  
Segmentation Task ◽  
Convolutional Encoder

Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

scholarly journals Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Muhammad Shahzad ◽  
Arif Iqbal Umar ◽  
Muazzam A. Khan ◽  
Syed Hamad Shirazi ◽  
Zakir Khan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Blood Cell ◽  
Network Performance ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Cell Segmentation ◽  
Blood Cell Type ◽  
Segmentation Approach ◽  
Convolutional Encoder ◽  
Extraction Model

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

scholarly journals A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters

Solar Energy ◽  
2021 ◽  
Vol 223 ◽  
pp. 217-228
Author(s):  
Amirmohammad Moradi Sizkouhi ◽  
Mohammadreza Aghaei ◽  
Sayyed Majid Esmailifar
Keyword(s):  
Fault Detection ◽  
Decoder Architecture ◽  
Convolutional Encoder
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